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    <title language="deu">Forschungsbericht 2015</title>
    <identifier type="isbn">978-3-00-048589-3</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-13867</identifier>
    <identifier type="doi">10.35096/othr/pub-1386</identifier>
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    <author>Jürgen Mottok</author>
    <author>Christina Artmann</author>
    <author>Martin Hobelsberger</author>
    <author>Ivan Paric</author>
    <author>Benjamin Großmann</author>
    <author>Clemens Pohlt</author>
    <author>Alena Wackerbarth</author>
    <author>Uli Pausch</author>
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    <author>Daniel Pöcher</author>
    <author>Lenz Pittroff</author>
    <author>Stefan Beer</author>
    <author>Oliver Brückl</author>
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    <author>Fabian Eckert</author>
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    <author>Erol Simsek</author>
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    <title language="deu">Forschungsbericht 2016</title>
    <identifier type="doi">10.35096/othr/pub-1384</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-13840</identifier>
    <author>Wolfgang Mauerer</author>
    <author>Tanja Rexhepaj</author>
    <author>Gareth J. Monkman</author>
    <author>Dirk Sindersberger</author>
    <author>Andreas Diermeier</author>
    <author>Thomas Neidhart</author>
    <author>Dominik Wolfrum</author>
    <author>Michael Sterner</author>
    <author>Michael Heberl</author>
    <author>Robert Nusko</author>
    <author>Georg Maier</author>
    <author>Klaus Nagl</author>
    <author>Monika Reuter</author>
    <author>Andreas Hofrichter</author>
    <author>Thomas Lex</author>
    <author>Florian Lesch</author>
    <author>Bärbel Kieninger</author>
    <author>Alexander Eduard Szalo</author>
    <author>Alexander Zehner</author>
    <author>Christoph Palm</author>
    <author>Mitchell Joblin</author>
    <author>Sven Apel</author>
    <author>Ralf Ramsauer</author>
    <author>Daniel Lohmann</author>
    <author>Markus Westner</author>
    <author>Artur Strasser</author>
    <author>Maximilian Munndi</author>
    <author>Lena Ebner</author>
    <author>Michael Elsner</author>
    <author>Nils Weiß</author>
    <author>Matthias Segerer</author>
    <author>Rudolf Hackenberg</author>
    <author>Sebastian Steger</author>
    <author>Anton Schmailzl</author>
    <author>Michael Dostalek</author>
    <author>Dominik Armbruster</author>
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    <author>Stefan Hierl</author>
    <author>Philipp Thumann</author>
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    <author>Ulrich Briem</author>
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    <author>Christian Hook</author>
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    <author>Tim Weber</author>
    <author>Simon Groß</author>
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    <author>Ingo Schillitz</author>
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    <author>Thomas Liebetruth</author>
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    <author>Wolfgang Kusterle</author>
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    <author>Johannes Stierstorfer</author>
    <author>Dominik Stadler</author>
    <author>Matthias J. Hofmann</author>
    <author>Hubert Motschmann</author>
    <author>Mikhail Shamonin (Chamonine)</author>
    <author>Veronika Bleicher</author>
    <author>Sebastian Fischer</author>
    <author>Rudolf Hackenberg</author>
    <author>Anton Horn</author>
    <author>Raphael Kawasch</author>
    <author>Michael Petzenhauser</author>
    <author>Tobias Probst</author>
    <author>Anton Udalzow</author>
    <author>Florian Dams</author>
    <author>Rupert Schreiner</author>
    <author>Christoph Langer</author>
    <author>Christian Prommesberger</author>
    <author>Robert Damian Ławrowski</author>
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      <title>Forschungsberichte der OTH Regensburg</title>
      <number>2016</number>
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  <doc>
    <id>783</id>
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    <publishedYear>2012</publishedYear>
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    <language>deu</language>
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    <pageLast/>
    <pageNumber>64</pageNumber>
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    <type>periodicalpart</type>
    <publisherName/>
    <publisherPlace>Regensburg</publisherPlace>
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    <title language="deu">Forschungsbericht 2012</title>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-7834</identifier>
    <identifier type="doi">10.35096/othr/pub-783</identifier>
    <author>Toni Lautenschläger</author>
    <author>Alexander Leis</author>
    <author>Sebastian Dendorfer</author>
    <author>Christoph Palm</author>
    <author>Rupert Schreiner</author>
    <author>Christoph Langer</author>
    <author>Christian Prommesberger</author>
    <author>Robert Damian Ławrowski</author>
    <author>Florian Dams</author>
    <author>Benjamin Bornmann</author>
    <author>Aliaksandr Navitski</author>
    <author>Pavel Serbun</author>
    <author>Günter Müller</author>
    <author>Thomas Liebetruth</author>
    <author>Dieter Kohlert</author>
    <author>Jochen Pernsteiner</author>
    <author>Franz Schreier</author>
    <author>Sabrina Heerklotz</author>
    <author>Allwin Heerklotz</author>
    <author>Alexander Boos</author>
    <author>Dominik Herwald</author>
    <author>Gareth J. Monkman</author>
    <author>Daniel Treiber</author>
    <author>Matthias Mayer</author>
    <author>Eva Hörner</author>
    <author>Alexander Bentz</author>
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  <doc>
    <id>789</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>72</pageNumber>
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    <volume/>
    <type>periodicalpart</type>
    <publisherName/>
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    <completedDate>2019-06-30</completedDate>
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    <title language="deu">Forschung 2019</title>
    <abstract language="deu">Bericht mit Forschungsprojekten aus verschiedenen Bereichen der OTH Regensburg mit dem Schwerpunktthema "Künstliche Intelligenz" und einem Gespräch zur "Medizin der Zukunft"</abstract>
    <subTitle language="deu">Thema: Künstliche Intelligenz</subTitle>
    <identifier type="isbn">978-3-9818209-7-3</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-7890</identifier>
    <identifier type="doi">10.35096/othr/pub-789</identifier>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Marie-Luise Appelhans</author>
    <author>Matthias Kampmann</author>
    <author>Jürgen Mottok</author>
    <author>Michael Riederer</author>
    <author>Klaus Nagl</author>
    <author>Oliver Steffens</author>
    <author>Jan Dünnweber</author>
    <author>Markus Wildgruber</author>
    <author>Julius Roth</author>
    <author>Timo Stadler</author>
    <author>Christoph Palm</author>
    <author>Martin Georg Weiß</author>
    <author>Sandra Rochholz</author>
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    <author>Andreas Gschossmann</author>
    <author>Sonja Haug</author>
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    <author>Markus Westner</author>
    <author>Benedikt von Bary</author>
    <author>Andreas Ellermeier</author>
    <author>Daniel Vögele</author>
    <author>Frederik Maiwald</author>
    <author>Stefan Hierl</author>
    <author>Matthias Schlamp</author>
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    <author>Michael Sterner</author>
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    <author>Fabian Eckert</author>
    <author>Franz Bauer</author>
    <author>Belal Dawoud</author>
    <author>Hans-Peter Rabl</author>
    <author>Bernd Gamisch</author>
    <author>Ottfried Schmidt</author>
    <author>Michael Heberl</author>
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    <author>Johannes Eller</author>
    <author>Thomas Sippenauer</author>
    <author>Christian Adelt</author>
    <author>Matthias Haslbeck</author>
    <author>Bettina Vogl</author>
    <author>Wolfgang Mauerer</author>
    <author>Ralf Ramsauer</author>
    <author>Daniel Lohmann</author>
    <author>Irmengard Sax</author>
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    <author>Sebastian Feld</author>
    <author>Claudia Linnhoff-Popien</author>
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    <series>
      <title>Forschungsberichte der OTH Regensburg</title>
      <number>2019</number>
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    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Forschung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Forschungsbericht</value>
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    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Künstliche Intelligenz</value>
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    <collection role="persons" number="bruecklfenes">Brückl, Oliver (Prof. Dr.) - FENES / Forschungsgruppe Energienetze</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/789/Forschungsbericht_2019.pdf</file>
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    <publishedYear>2017</publishedYear>
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    <title language="deu">Forschungsbericht 2017</title>
    <identifier type="isbn">978-3-9818209-3-5</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-13835</identifier>
    <identifier type="doi">10.35096/othr/pub-1383</identifier>
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    <author>Franz Süß</author>
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    <author>Thomas Poxleitner</author>
    <author>Simon Hofer</author>
    <author>Benjamin Heisterkamp</author>
    <author>Maximilian Lerch</author>
    <author>Nike Sammer</author>
    <author>Olivia Golde</author>
    <author>Felix Wellnitz</author>
    <author>Sandra Schmid</author>
    <author>Claudia Muntschick</author>
    <author>Wolfgang Kusterle</author>
    <author>Ivan Paric</author>
    <author>Oliver Brückl</author>
    <author>Matthias Haslbeck</author>
    <author>Ottfried Schmidt</author>
    <author>Peter Schwanzer</author>
    <author>Hans-Peter Rabl</author>
    <author>Michael Sterner</author>
    <author>Franz Bauer</author>
    <author>Sven Steinmann</author>
    <author>Fabian Eckert</author>
    <author>Andreas Hofrichter</author>
    <series>
      <title>Forschungsberichte der OTH Regensburg</title>
      <number>2017</number>
    </series>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Forschung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Forschungsbericht</value>
    </subject>
    <collection role="institutes" number="HL">Hochschulleitung/Hochschulverwaltung</collection>
    <collection role="othpublikationsherkunft" number="">Von der OTH Regensburg herausgegeben</collection>
    <collection role="persons" number="weberlate">Weber, Karsten (Prof. Dr.) - Labor für Technikfolgenabschätzung und Angewandte Ethik</collection>
    <collection role="institutes" number="IAFW">Zentrum für Forschung und Transfer (ZFT ab 2024; vorher: IAFW)</collection>
    <collection role="persons" number="sternerfenes">Sterner, Michael (Prof. Dr.) - FENES / Forschungsgruppe Energiespeicher</collection>
    <collection role="persons" number="bruecklfenes">Brückl, Oliver (Prof. Dr.) - FENES / Forschungsgruppe Energienetze</collection>
    <collection role="persons" number="hauglasofo">Haug, Sonja (Prof. Dr.) - Labor Empirische Sozialforschung</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/1383/Forschungsbericht_OTHR_2017.pdf</file>
  </doc>
  <doc>
    <id>137</id>
    <completedYear/>
    <publishedYear>2008</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1142</pageFirst>
    <pageLast>1146</pageLast>
    <pageNumber/>
    <edition/>
    <issue>6</issue>
    <volume>35</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">MR-based attenuation correction for torso-PET/MR imaging</title>
    <abstract language="eng">Purpose&#13;
MR-based attenuation correction (AC) will become an integral part of combined PET/MR systems. Here, we propose a toolbox to validate MR-AC of clinical PET/MRI data sets.&#13;
Methods&#13;
Torso scans of ten patients were acquired on a combined PET/CT and on a 1.5-T MRI system. MR-based attenuation data were derived from the CT following MR–CT image co-registration and subsequent histogram matching. PET images were reconstructed after CT- (PET/CT) and MR-based AC (PET/MRI). Lesion-to-background (L/B) ratios were estimated on PET/CT and PET/MRI.&#13;
Results&#13;
MR–CT histogram matching leads to a mean voxel intensity difference in the CT- and MR-based attenuation images of 12% (max). Mean differences between PET/MRI and PET/CT were 19% (max). L/B ratios were similar except for the lung where local misregistration and intensity transformation leads to a biased PET/MRI.&#13;
Conclusion&#13;
Our toolbox can be used to study pitfalls in MR-AC. We found that co-registration accuracy and pixel value transformation determine the accuracy of PET/MRI.</abstract>
    <parentTitle language="eng">European Journal of Nuclear Medicine and Molecular Imaging</parentTitle>
    <subTitle language="eng">pitfalls in mapping MR to CT data</subTitle>
    <identifier type="doi">10.1007/s00259-008-0734-0</identifier>
    <author>Thomas Beyer</author>
    <author>Markus Weigert</author>
    <author>Harald H. Quick</author>
    <author>Uwe Pietrzyk</author>
    <author>Florian Vogt</author>
    <author>Christoph Palm</author>
    <author>Gerald Antoch</author>
    <author>Stefan P. Müller</author>
    <author>Andreas Bockisch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>PET/MRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>PET/CT</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Attenuation correction</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Kernspintomografie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Positronen-Emissions-Tomografie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schwächung</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>7034</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>330</pageFirst>
    <pageLast>343</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>39</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-02-19</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Utility of Smartphone-based Three-dimensional Surface Imaging for Digital Facial Anthropometry</title>
    <abstract language="eng">Background&#13;
The utilization of three-dimensional (3D) surface imaging for facial anthropometry is a significant asset for patients undergoing maxillofacial surgery. Notably, there have been recent advancements in smartphone technology that enable 3D surface imaging.&#13;
&#13;
In this study, anthropometric assessments of the face were performed using a smartphone and a sophisticated 3D surface imaging system.&#13;
&#13;
Methods&#13;
30 healthy volunteers (15 females and 15 males) were included in the study. An iPhone 14 Pro (Apple Inc., USA) using the application 3D Scanner App (Laan Consulting Corp., USA) and the Vectra M5 (Canfield Scientific, USA) were employed to create 3D surface models. For each participant, 19 anthropometric measurements were conducted on the 3D surface models. Subsequently, the anthropometric measurements generated by the two approaches were compared. The statistical techniques employed included the paired t-test, paired Wilcoxon signed-rank test, Bland–Altman analysis, and calculation of the intraclass correlation coefficient (ICC).&#13;
&#13;
Results&#13;
All measurements showed excellent agreement between smartphone-based and Vectra M5-based measurements (ICC between 0.85 and 0.97). Statistical analysis revealed no statistically significant differences in the central tendencies for 17 of the 19 linear measurements. Despite the excellent agreement found, Bland–Altman analysis revealed that the 95% limits of agreement between the two methods exceeded ±3 mm for the majority of measurements.&#13;
&#13;
Conclusion&#13;
Digital facial anthropometry using smartphones can serve as a valuable supplementary tool for surgeons, enhancing their communication with patients. However, the proposed data suggest that digital facial anthropometry using smartphones may not yet be suitable for certain diagnostic purposes that require high accuracy.</abstract>
    <parentTitle language="eng">JPRAS Open</parentTitle>
    <identifier type="doi">10.1016/j.jpra.2024.01.014</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-70348</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Robin Hartmann</author>
    <author>Felix Nieberle</author>
    <author>Christoph Palm</author>
    <author>Vanessa Brébant</author>
    <author>Lukas Prantl</author>
    <author>Reinald Kuehle</author>
    <author>Torsten E. Reichert</author>
    <author>Juergen Taxis</author>
    <author>Tobias Ettl</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Three-dimensional surface imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Stereophotogrammetry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Smartphone-based surface imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital anthropometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Facial anthropometry</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/7034/1-s2.0-S2352587824000159-main.pdf</file>
  </doc>
  <doc>
    <id>5429</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2388</pageFirst>
    <pageLast>2390</pageLast>
    <pageNumber/>
    <edition/>
    <issue>12</issue>
    <volume>71</volume>
    <type>article</type>
    <publisherName>BMJ</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-09-16</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm</title>
    <abstract language="eng">In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63% and 76%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training.</abstract>
    <parentTitle language="eng">Gut</parentTitle>
    <identifier type="doi">10.1136/gutjnl-2021-326470</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-54293</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International</licence>
    <author>Alanna Ebigbo</author>
    <author>Robert Mendel</author>
    <author>Markus W. Scheppach</author>
    <author>Andreas Probst</author>
    <author>Neal Shahidi</author>
    <author>Friederike Prinz</author>
    <author>Carola Fleischmann</author>
    <author>Christoph Römmele</author>
    <author>Stefan Karl Gölder</author>
    <author>Georg Braun</author>
    <author>David Rauber</author>
    <author>Tobias Rückert</author>
    <author>Luis Antonio de Souza Jr.</author>
    <author>João Paulo Papa</author>
    <author>Michael F. Byrne</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Endoscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Medical Image Computing</value>
    </subject>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="ddc" number="617">Chirurgie und verwandte medizinische Fachrichtungen</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/5429/gutjnl-2021-326470.full_supp.pdf</file>
  </doc>
  <doc>
    <id>5718</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>17</issue>
    <volume>11</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-25</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science</title>
    <abstract language="eng">Background: &#13;
Reliable, time- and cost-effective, and clinician-friendly diagnostic tools are cornerstones in facial palsy (FP) patient management. Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We aimed to overcome these barriers and programmed an automated grading system for FP patients utilizing the House and Brackmann scale (HBS). &#13;
Methods: &#13;
Image datasets of 86 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2017 and May 2021, were used to train the neural network and evaluate its accuracy. Nine facial poses per patient were analyzed by the algorithm. &#13;
Results: &#13;
The algorithm showed an accuracy of 100%. Oversampling did not result in altered outcomes, while the direct form displayed superior accuracy levels when compared to the modular classification form (n = 86; 100% vs. 99%). The Early Fusion technique was linked to improved accuracy outcomes in comparison to the Late Fusion and sequential method (n = 86; 100% vs. 96% vs. 97%). &#13;
Conclusions: &#13;
Our automated FP grading system combines high-level accuracy with cost- and time-effectiveness. Our algorithm may accelerate the grading process in FP patients and facilitate the FP surgeon’s workflow.</abstract>
    <parentTitle language="eng">Journal of Clinical Medicine</parentTitle>
    <identifier type="doi">10.3390/jcm11174998</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Leonard Knödler</author>
    <author>Helena Baecher</author>
    <author>Martin Kauke-Navarro</author>
    <author>Lukas Prantl</author>
    <author>Hans-Günther Machens</author>
    <author>Philipp Scheuermann</author>
    <author>Christoph Palm</author>
    <author>Raphael Baumann</author>
    <author>Andreas Kehrer</author>
    <author>Adriana C. Panayi</author>
    <author>Samuel Knoedler</author>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="61">Medizin und Gesundheit</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>102</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>203</pageFirst>
    <pageLast>213</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>96</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-12-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A survey on Barrett's esophagus analysis using machine learning</title>
    <abstract language="eng">This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods.</abstract>
    <parentTitle language="eng">Computers in Biology and Medicine</parentTitle>
    <identifier type="doi">10.1016/j.compbiomed.2018.03.014</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Luis Antonio de Souza Jr.</author>
    <author>Christoph Palm</author>
    <author>Robert Mendel</author>
    <author>Christian Hook</author>
    <author>Alanna Ebigbo</author>
    <author>Andreas Probst</author>
    <author>Helmut Messmann</author>
    <author>Silke A. T. Weber</author>
    <author>João Paulo Papa</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Speiseröhrenkrankheit</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Diagnose</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Mustererkennung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Literaturbericht</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Barrett's esophagus</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Adenocarcinoma</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image processing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pattern recognition</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Computer-aided diagnosis</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>611</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>e11351</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>6</issue>
    <volume>8</volume>
    <type>article</type>
    <publisherName>Wiley, Botanical Society of America</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-07-07</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">GinJinn: An object-detection pipeline for automated feature extraction from herbarium specimens</title>
    <abstract language="eng">PREMISE: &#13;
The generation of morphological data in evolutionary, taxonomic, and ecological studies of plants using herbarium material has traditionally been a labor-intensive task. Recent progress in machine learning using deep artificial neural networks (deep learning) for image classification and object detection has facilitated the establishment of a pipeline for the automatic recognition and extraction of relevant structures in images of herbarium specimens.&#13;
&#13;
METHODS AND RESULTS: &#13;
We implemented an extendable pipeline based on state-of-the-art deep-learning object-detection methods to collect leaf images from herbarium specimens of two species of the genus Leucanthemum. Using 183 specimens as the training data set, our pipeline extracted one or more intact leaves in 95% of the 61 test images.&#13;
&#13;
CONCLUSIONS:&#13;
We establish GinJinn as a deep-learning object-detection tool for the automatic recognition and extraction of individual leaves or other structures from herbarium specimens. Our pipeline offers greater flexibility and a lower entrance barrier than previous image-processing approaches based on hand-crafted features.</abstract>
    <parentTitle language="eng">Applications in Plant Sciences</parentTitle>
    <identifier type="doi">10.1002/aps3.11351</identifier>
    <identifier type="issn">2168-0450</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Tankred Ott</author>
    <author>Christoph Palm</author>
    <author>Robert Vogt</author>
    <author>Christoph Oberprieler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>herbarium specimens</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>object detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>visual recognition</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Objekterkennung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Sehen</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Pflanzen</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="5">Naturwissenschaften und Mathematik</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>354</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1616</pageFirst>
    <pageLast>1623</pageLast>
    <pageNumber/>
    <edition/>
    <issue>12</issue>
    <volume>07</volume>
    <type>article</type>
    <publisherName>Georg Thieme Verlag</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-04-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology</title>
    <abstract language="eng">The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research.&#13;
&#13;
In gastrointestinal endoscopy, AI has been applied to all segments of the gastrointestinal tract most importantly in the detection and characterization of colorectal polyps. However, AI research has been published also in the stomach and esophagus for both neoplastic and non-neoplastic disorders.&#13;
&#13;
The various technical as well as medical aspects of AI, however, remain confusing especially for non-expert physicians.&#13;
&#13;
This physician-engineer co-authored review explains the basic technical aspects of AI and provides a comprehensive overview of recent publications on AI in gastrointestinal endoscopy. Finally, a basic insight is offered into understanding publications on AI in gastrointestinal endoscopy.</abstract>
    <parentTitle language="eng">Endoscopy International Open</parentTitle>
    <identifier type="doi">10.1055/a-1010-5705</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Alanna Ebigbo</author>
    <author>Christoph Palm</author>
    <author>Andreas Probst</author>
    <author>Robert Mendel</author>
    <author>Johannes Manzeneder</author>
    <author>Friederike Prinz</author>
    <author>Luis Antonio de Souza Jr.</author>
    <author>João Paulo Papa</author>
    <author>Peter Siersema</author>
    <author>Helmut Messmann</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Diagnose</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gastroenterologie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Barrett's esophagus</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>109</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>122</pageFirst>
    <pageLast>127</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The Impact of Semi-Automated Segmentation and 3D Analysis on Testing New Osteosynthesis Material</title>
    <abstract language="eng">A new protocol for testing osteosynthesis material postoperatively combining semi-automated segmentation and 3D analysis of surface meshes is proposed. By various steps of transformation and measuring, objective data can be collected. In this study the specifications of a locking plate used for mediocarpal arthrodesis of the wrist were examined. The results show, that union of the lunate, triquetrum, hamate and capitate was achieved and that the plate is comparable to coexisting arthrodesis systems. Additionally, it was shown, that the complications detected correlate to the clinical outcome. In synopsis, this protocol is considered beneficial and should be taken into account in further studies.</abstract>
    <parentTitle language="deu">Bildverarbeitung für die Medizin 2017; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 12. bis 14. März 2017 in Heidelberg</parentTitle>
    <identifier type="doi">10.1007/978-3-662-54345-0_30</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Rebecca Wöhl</author>
    <author>Michaela Huber</author>
    <author>Markus Loibl</author>
    <author>Birgit Riebschläger</author>
    <author>Michael Nerlich</author>
    <author>Christoph Palm</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Osteosynthese</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Implantatwerkstoff</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Materialprüfung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildsegmentierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Dreidimensionale Bildverarbeitung</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>118</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Fusion of Serial 2D Section Images and MRI Reference</title>
    <abstract language="eng">Serial 2D section images with high resolution, resulting from innovative imaging methods become even more valuable, if they are fused with in vivo volumes. Achieving this goal, the 3D context of the sections would be restored, the deformations would be corrected and the artefacts would be eliminated. However, the registration in this field faces big challenges and is not solved in general. On the other hand, several approaches have been introduced dealing at least with some of these difficulties. Here, a brief overview of the topic is given and some of the solutions are presented. It does not constitute the claim to be a complete review, but could be a starting point for those who are interested in this field.</abstract>
    <parentTitle language="eng">Workshop Innovative Verarbeitung bioelektrischer und biomagnetischer Signale (bbs2014), Berlin, 10.04.2014</parentTitle>
    <subTitle language="eng">an Overview</subTitle>
    <identifier type="doi">10.13140/RG.2.1.1358.3449</identifier>
    <author>Christoph Palm</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Kernspintomografie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Optimierung</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Magnetic Resonance Imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MRI</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Literaturbericht</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="oaweg" number="">Diamond Open Access - OA-Veröffentlichung ohne Publikationskosten (Sponsoring)</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>7281</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>641</pageFirst>
    <pageLast>649</pageLast>
    <pageNumber>9</pageNumber>
    <edition/>
    <issue/>
    <volume>56</volume>
    <type>article</type>
    <publisherName>Georg Thieme Verlag</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-05-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett’s esophagus: a tandem randomized and video trial</title>
    <abstract language="eng">Background &#13;
This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett’s esophagus (BE).&#13;
&#13;
Methods &#13;
96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett’s esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level.&#13;
&#13;
Results &#13;
AI had a stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1%, and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8% [95%CI 65.2%–74.2%] to 78.0% [95%CI 74.0%–82.0%]; specificity 67.3% [95%CI 62.5%–72.2%] to 72.7% [95%CI 68.2%–77.3%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI.&#13;
&#13;
Conclusion &#13;
BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists’ decisions to follow or discard AI advice.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/a-2296-5696</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-72818</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Michael Meinikheim</author>
    <author>Robert Mendel</author>
    <author>Christoph Palm</author>
    <author>Andreas Probst</author>
    <author>Anna Muzalyova</author>
    <author>Markus W. Scheppach</author>
    <author>Sandra Nagl</author>
    <author>Elisabeth Schnoy</author>
    <author>Christoph Römmele</author>
    <author>Dominik Andreas Helmut Otto Schulz</author>
    <author>Jakob Schlottmann</author>
    <author>Friederike Prinz</author>
    <author>David Rauber</author>
    <author>Tobias Rückert</author>
    <author>Tomoaki Matsumura</author>
    <author>Glòria Fernández-Esparrach</author>
    <author>Nasim Parsa</author>
    <author>Michael F. Byrne</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Endoscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Medical Image Computing</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/7281/Meinikheim_Mendel_Endoscopy_2024.pdf</file>
  </doc>
  <doc>
    <id>7276</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>S428</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>S 02</issue>
    <volume>56</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-05-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Early Esophageal Cancer and the Generalizability of Artificial Intelligence</title>
    <abstract language="eng">Aims &#13;
Artificial Intelligence (AI) systems in gastrointestinal endoscopy are narrow because they are trained to solve only one specific task. Unlike Narrow-AI, general AI systems may be able to solve multiple and unrelated tasks. We aimed to understand whether an AI system trained to detect, characterize, and segment early Barrett’s neoplasia (Barrett’s AI) is only capable of detecting this pathology or can also detect and segment other diseases like early squamous cell cancer (SCC).&#13;
&#13;
Methods &#13;
120 white light (WL) and narrow-band endoscopic images (NBI) from 60 patients (1 WL and 1 NBI image per patient) were extracted from the endoscopic database of the University Hospital Augsburg. Images were annotated by three expert endoscopists with extensive experience in the diagnosis and endoscopic resection of early esophageal neoplasias. An AI system based on DeepLabV3+architecture dedicated to early Barrett’s neoplasia was tested on these images. The AI system was neither trained with SCC images nor had it seen the test images prior to evaluation. The overlap between the three expert annotations („expert-agreement“) was the ground truth for evaluating AI performance.&#13;
&#13;
Results &#13;
Barrett’s AI detected early SCC with a mean intersection over reference (IoR) of 92% when at least 1 pixel of the AI prediction overlapped with the expert-agreement. When the threshold was increased to 5%, 10%, and 20% overlap with the expert-agreement, the IoR was 88%, 85% and 82%, respectively. The mean Intersection Over Union (IoU) – a metric according to segmentation quality between the AI prediction and the expert-agreement – was 0.45. The mean expert IoU as a measure of agreement between the three experts was 0.60.&#13;
&#13;
Conclusions &#13;
In the context of this pilot study, the predictions of SCC by a Barrett’s dedicated AI showed some overlap to the expert-agreement. Therefore, features learned from Barrett’s cancer-related training might be helpful also for SCC prediction. Our results allow different possible explanations. On the one hand, some Barrett’s cancer features generalize toward the related task of assessing early SCC. On the other hand, the Barrett’s AI is less specific to Barrett’s cancer than a general predictor of pathological tissue. However, we expect to enhance the detection quality significantly by extending the training to SCC-specific data. The insight of this study opens the way towards a transfer learning approach for more efficient training of AI to solve tasks in other domains.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/s-0044-1783775</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2024</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Alanna Ebigbo</author>
    <author>David Rauber</author>
    <author>Mousa Ayoub</author>
    <author>Lisa Birzle</author>
    <author>Tomoaki Matsumura</author>
    <author>Andreas Probst</author>
    <author>Ingo Steinbrück</author>
    <author>Sandra Nagl</author>
    <author>Christoph Römmele</author>
    <author>Michael Meinikheim</author>
    <author>Markus W. Scheppach</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>7277</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>S93</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>S 02</issue>
    <volume>56</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-05-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Artificial Intelligence (AI) improves endoscopists’ vessel detection during endoscopic submucosal dissection (ESD)</title>
    <abstract language="eng">Aims &#13;
While AI has been successfully implemented in detecting and characterizing colonic polyps, its role in therapeutic endoscopy remains to be elucidated. Especially third space endoscopy procedures like ESD and peroral endoscopic myotomy (POEM) pose a technical challenge and the risk of operator-dependent complications like intraprocedural bleeding and perforation. Therefore, we aimed at developing an AI-algorithm for intraprocedural real time vessel detection during ESD and POEM.&#13;
&#13;
Methods &#13;
A training dataset consisting of 5470 annotated still images from 59 full-length videos (47 ESD, 12 POEM) and 179681 unlabeled images was used to train a DeepLabV3+neural network with the ECMT semi-supervised learning method. Evaluation for vessel detection rate (VDR) and time (VDT) of 19 endoscopists with and without AI-support was performed using a testing dataset of 101 standardized video clips with 200 predefined blood vessels. Endoscopists were stratified into trainees and experts in third space endoscopy.&#13;
&#13;
Results &#13;
The AI algorithm had a mean VDR of 93.5% and a median VDT of 0.32 seconds. AI support was associated with a statistically significant increase in VDR from 54.9% to 73.0% and from 59.0% to 74.1% for trainees and experts, respectively. VDT significantly decreased from 7.21 sec to 5.09 sec for trainees and from 6.10 sec to 5.38 sec for experts in the AI-support group. False positive (FP) readings occurred in 4.5% of frames. FP structures were detected significantly shorter than true positives (0.71 sec vs. 5.99 sec).&#13;
&#13;
Conclusions &#13;
AI improved VDR and VDT of trainees and experts in third space endoscopy and may reduce performance variability during training. Further research is needed to evaluate the clinical impact of this new technology.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/s-0044-1782891</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2024</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus W. Scheppach</author>
    <author>Robert Mendel</author>
    <author>David Rauber</author>
    <author>Andreas Probst</author>
    <author>Sandra Nagl</author>
    <author>Christoph Römmele</author>
    <author>Michael Meinikheim</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>7275</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>S198</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>S 02</issue>
    <volume>56</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-05-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Artificial intelligence as a tool in the detection of the papillary ostium during ERCP</title>
    <abstract language="eng">Aims &#13;
Endoscopic retrograde cholangiopancreaticography (ERCP) is the gold standard in the diagnosis as well as treatment of diseases of the pancreatobiliary tract. However, it is technically complex and has a relatively high complication rate. In particular, cannulation of the papillary ostium remains challenging. The aim of this study is to examine whether a deep-learning algorithm can be used to detect the major duodenal papilla and in particular the papillary ostium reliably and could therefore be a valuable tool for inexperienced endoscopists, particularly in training situation.&#13;
&#13;
Methods &#13;
We analyzed a total of 654 retrospectively collected images of 85 patients. Both the major duodenal papilla and the ostium were then segmented. Afterwards, a neural network was trained using a deep-learning algorithm. A 5-fold cross-validation was performed. Subsequently, we ran the algorithm on 5 prospectively collected videos of ERCPs.&#13;
&#13;
Results&#13;
5-fold cross-validation on the 654 labeled data resulted in an F1 value of 0.8007, a sensitivity of 0.8409 and a specificity of 0.9757 for the class papilla, and an F1 value of 0.5724, a sensitivity of 0.5456 and a specificity of 0.9966 for the class ostium. Regardless of the class, the average F1 value (class papilla and class ostium) was 0.6866, the sensitivity 0.6933 and the specificity 0.9861. In 100% of cases the AI-detected localization of the papillary ostium in the prospectively collected videos corresponded to the localization of the cannulation performed by the endoscopist.&#13;
&#13;
Conclusions &#13;
In the present study, the neural network was able to identify the major duodenal papilla with a high sensitivity and high specificity. In detecting the papillary ostium, the sensitivity was notably lower. However, when used on videos, the AI was able to identify the location of the subsequent cannulation with 100% accuracy. In the future, the neural network will be trained with more data. Thus, a suitable tool for ERCP could be established, especially in the training situation.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/s-0044-1783138</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2024</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Stephan Zellmer</author>
    <author>David Rauber</author>
    <author>Andreas Probst</author>
    <author>Tobias Weber</author>
    <author>Georg Braun</author>
    <author>Christoph Römmele</author>
    <author>Sandra Nagl</author>
    <author>Elisabeth Schnoy</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <author>Christoph Palm</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>7278</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>S439</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>S 02</issue>
    <volume>56</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-05-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Procedural phase recognition in endoscopic submucosal dissection (ESD) using artificial intelligence (AI)</title>
    <abstract language="eng">Aims &#13;
Recent evidence suggests the possibility of intraprocedural phase recognition in surgical operations as well as endoscopic interventions such as peroral endoscopic myotomy and endoscopic submucosal dissection (ESD) by AI-algorithms. The intricate measurement of intraprocedural phase distribution may deepen the understanding of the procedure. Furthermore, real-time quality assessment as well as automation of reporting may become possible. Therefore, we aimed to develop an AI-algorithm for intraprocedural phase recognition during ESD.&#13;
&#13;
Methods &#13;
A training dataset of 364385 single images from 9 full-length ESD videos was compiled. Each frame was classified into one procedural phase. Phases included scope manipulation, marking, injection, application of electrical current and bleeding. Allocation of each frame was only possible to one category. This training dataset was used to train a Video Swin transformer to recognize the phases. Temporal information was included via logarithmic frame sampling. Validation was performed using two separate ESD videos with 29801 single frames.&#13;
&#13;
Results &#13;
The validation yielded sensitivities of 97.81%, 97.83%, 95.53%, 85.01% and 87.55% for scope manipulation, marking, injection, electric application and bleeding, respectively. Specificities of 77.78%, 90.91%, 95.91%, 93.65% and 84.76% were measured for the same parameters.&#13;
&#13;
Conclusions &#13;
The developed algorithm was able to classify full-length ESD videos on a frame-by-frame basis into the predefined classes with high sensitivities and specificities. Future research will aim at the development of quality metrics based on single-operator phase distribution.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/s-0044-1783804</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2024</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus W. Scheppach</author>
    <author>Danilo Weber Nunes</author>
    <author>X. Arizi</author>
    <author>David Rauber</author>
    <author>Andreas Probst</author>
    <author>Sandra Nagl</author>
    <author>Christoph Römmele</author>
    <author>Michael Meinikheim</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>6039</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>S165</pageNumber>
    <edition/>
    <issue>S02</issue>
    <volume>55</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-05-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Performance comparison of a deep learning algorithm with endoscopists in the detection of duodenal villous atrophy (VA)</title>
    <abstract language="eng">Aims &#13;
VA is an endoscopic finding of celiac disease (CD), which can easily be missed if pretest probability is low. In this study, we aimed to develop an artificial intelligence (AI) algorithm for the detection of villous atrophy on endoscopic images.&#13;
&#13;
Methods&#13;
858 images from 182 patients with VA and 846 images from 323 patients with normal duodenal mucosa were used for training and internal validation of an AI algorithm (ResNet18). A separate dataset was used for external validation, as well as determination of detection performance of experts, trainees and trainees with AI support. According to the AI consultation distribution, images were stratified into “easy” and “difficult”.&#13;
&#13;
Results&#13;
Internal validation showed 82%, 85% and 84% for sensitivity, specificity and accuracy. External validation showed 90%, 76% and 84%. The algorithm was significantly more sensitive and accurate than trainees, trainees with AI support and experts in endoscopy. AI support in trainees was associated with significantly improved performance. While all endoscopists showed significantly lower detection for “difficult” images, AI performance remained stable.&#13;
&#13;
Conclusions&#13;
The algorithm outperformed trainees and experts in sensitivity and accuracy for VA detection. The significant improvement with AI support suggests a potential clinical benefit. Stable performance of the algorithm in “easy” and “difficult” test images may indicate an advantage in macroscopically challenging cases.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/s-0043-1765421</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2023</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus W. Scheppach</author>
    <author>David Rauber</author>
    <author>Johannes Stallhofer</author>
    <author>Anna Muzalyova</author>
    <author>Vera Otten</author>
    <author>Carolin Manzeneder</author>
    <author>Tanja Schwamberger</author>
    <author>Julia Wanzl</author>
    <author>Jakob Schlottmann</author>
    <author>Vidan Tadic</author>
    <author>Andreas Probst</author>
    <author>Elisabeth Schnoy</author>
    <author>Christoph Römmele</author>
    <author>Carola Fleischmann</author>
    <author>Michael Meinikheim</author>
    <author>Silvia Miller</author>
    <author>Bruno Märkl</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <collection role="ddc" number="61">Medizin und Gesundheit</collection>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>8056</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>S511</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>S 02</issue>
    <volume>57</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-04-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Single frame workflow recognition during endoscopic submucosal dissection (ESD) using artificial intelligence (AI)</title>
    <abstract language="eng">Aims &#13;
Precise surgical phase recognition and evaluation may improve our understanding of complex endoscopic procedures. Furthermore, quality control measurements and endoscopy training could benefit from objective descriptions of surgical phase distributions. Therefore, we aimed to develop an artificial intelligence algorithm for frame-by-frame operational phase recognition during endoscopic submucosal dissection (ESD).&#13;
&#13;
Methods &#13;
Full length ESD-videos from 31 patients comprising 6.297.782 single images were collected retrospectively. Videos were annotated on a frame-by-frame basis for the operational macro-phases diagnostics, marking, injection, dissection and bleeding. Further subphases were the application of electrical current, visible injection of fluid into the submucosal space and scope manipulation, leading to 11 phases in total. 4.975.699 frames (21 patients) were used for training of a video swin transformer using uniform frame sampling for temporal information. Hyperparameter tuning was performed with 897.325 further frames (6 patients), while 424.758 frames (4 patients) were used for validation.&#13;
&#13;
Results &#13;
The overall F1 scores on the test dataset for the macro-phases and all 11 phases were 0.96 and 0.90, respectively. The recall values for diagnostics, marking, injection, dissection and bleeding were 1.00, 1.00, 0.95, 0.96 and 0.93, respectively.&#13;
&#13;
Conclusions &#13;
The algorithm classified operational phases during ESD with high accuracy. A precise evaluation of phase distribution may allow for the development of objective quality metrics for quality control and training.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/s-0045-1806324</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2025</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus W. Scheppach</author>
    <author>Danilo Weber Nunes</author>
    <author>X. Arizi</author>
    <author>David Rauber</author>
    <author>Andreas Probst</author>
    <author>Sandra Nagl</author>
    <author>Christoph Römmele</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>141</id>
    <completedYear/>
    <publishedYear>2008</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>59</pageFirst>
    <pageLast>66</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>18</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Whole-body PET/CT imaging</title>
    <abstract language="eng">Aim&#13;
Combined whole-body (WB) PET/CT imaging provides better overall co-registration compared to separate CT and PET. However, in clinical routine local PET-CT mis-registration cannot be avoided. Thus, the reconstructed PET tracer distribution may be biased when using the misaligned CT transmission data for CT-based attenuation correction (CT-AC). We investigate the feasibility of retrospective co-registration techniques to align CT and PET images prior to CT-AC, thus improving potentially the quality of combined PET/CT imaging in clinical routine.&#13;
Methods&#13;
First, using a commercial software registration package CT images were aligned to the uncorrected PET data by rigid and non-rigid registration methods. Co-registration accuracy of both alignment approaches was assessed by reviewing the PET tracer uptake patterns (visual, linked cursor display) following attenuation correction based on the original and co-registered CT. Second, we investigated non-rigid registration based on a prototype ITK implementation of the B-spline algorithm on a similar targeted MR-CT registration task, there showing promising results.&#13;
Results&#13;
Manual rigid, landmark-based co-registration introduced unacceptable misalignment, in particular in peripheral areas of the whole-body images. Manual, non-rigid landmark-based co-registration prior to CT-AC was successful with minor loco-regional distortions. Nevertheless, neither rigid nor non-rigid automatic co-registration based on the Mutual Information image to image metric succeeded in co-registering the CT and noAC-PET images. In contrast to widely available commercial software registration our implementation of an alternative automated, non-rigid B-spline co-registration technique yielded promising results in this setting with MR-CT data.&#13;
Conclusion&#13;
In clinical PET/CT imaging, retrospective registration of CT and uncorrected PET images may improve the quality of the AC-PET images. As of today no validated and clinically viable commercial registration software is in routine use. This has triggered our efforts in pursuing new approaches to a validated, non-rigid co-registration algorithm applicable to whole-body PET/CT imaging of which first results are presented here. This approach appears suitable for applications in retrospective WB-PET/CT alignment.&#13;
&#13;
Ziel&#13;
Kombinierte PET/CT-Bildgebung ermöglicht verbesserte Koregistrierung von PET- und CT-Daten gegenüber separat akquirierten Bildern. Trotzdem entstehen in der klinischen Anwendung lokale Fehlregistrierungen, die zu Fehlern in der rekonstruierten PET- Tracerverteilung führen können, falls die unregistrierten CT-Daten zur Schwächungskorrektur (AC) der Emissionsdaten verwendet werden. Wir untersuchen daher die Anwendung von Bildregistrierungsalgorithmen vor der CT-basierten AC zur Verbesserung der PET-Aufnahmen.&#13;
Methoden&#13;
Mittels einer kommerziellen Registrierungssoftware wurden die CT-Daten eines PET/CT- Tomographen durch landmarken- und intensitätsbasierte rigide (starre) und nicht-rigide Registrierungsverfahren räumlich an die unkorrigierten PET-Emissionsdaten angepasst und zur AC verwendet. Zur Bewertung wurden die Tracerverteilungen in den PET-Bildern (vor AC, CT-AC, CT-AC nach Koregistrierung) visuell und mit Hilfe korrelierter Fadenkreuze verglichen. Zusätzlich untersuchten wir die ITK-Implementierung der bekannten B-spline basierten, nicht-rigiden Registrierungsansätze im Hinblick auf ihre Verwendbarkeit für die multimodale PET/CT-Ganzkörperregistrierung.&#13;
Ergebnisse&#13;
Mittels landmarkenbasierter, nicht-rigider Registrierung konnte die Tracerverteilung in den PET-Daten lokal verbessert werden. Landmarkenbasierte rigide Registrierung führte zu starker Fehlregistrierung in entfernten Körperregionen. Automatische rigide und nicht-rigide Registrierung unter Verwendung der Mutual-Information-Ähnlichkeitsmetrik versagte auf allen verwendeten Datensätzen. Die automatische Registrierung mit B-spline-Funktionen zeigte vielversprechende Resultate in der Anwendung auf einem ähnlich gelagerten CT–MR-Registrierungsproblem.&#13;
Fazit&#13;
Retrospektive, nicht-rigide Registrierung unkorrigierter PET- und CT-Aufnahmen aus kombinierten Aufnahmensystemen vor der AC kann die Qualität von PET-Aufnahmen im klinischen Einsatz verbessern. Trotzdem steht bis heute im klinischen Alltag keine validierte, automatische Registrierungssoftware zur Verfügung. Wir verfolgen dazu Ansätze für validierte, nicht-rigide Bildregistrierung für den klinischen Einsatz und präsentieren erste Ergebnisse.</abstract>
    <parentTitle language="deu">Zeitschrift für Medizinische Physik</parentTitle>
    <subTitle language="eng">Combining software- and hardware-based co-registration</subTitle>
    <identifier type="doi">10.1016/j.zemedi.2007.07.004</identifier>
    <author>Markus Weigert</author>
    <author>Uwe Pietrzyk</author>
    <author>Stefan P. Müller</author>
    <author>Christoph Palm</author>
    <author>Thomas Beyer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>PET/CT</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>combined imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>image co-registration</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>attenuation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>correction</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Positronen-Emissions-Tomografie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Computertomografie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Registrierung &lt;Bildverarbeitung&gt;</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schwächung</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>7261</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>79</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>S 02</issue>
    <volume>56</volume>
    <type>conferencepresentation</type>
    <publisherName>Georg Thieme Verlag</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Human-Computer Interaction: Impact of Artificial Intelligence on the diagnostic confidence of endoscopists assessing videos of Barrett’s esophagus</title>
    <abstract language="eng">Aims &#13;
Human-computer interactions (HCI) may have a relevant impact on the performance of Artificial Intelligence (AI). Studies show that although endoscopists assessing Barrett’s esophagus (BE) with AI improve their performance significantly, they do not achieve the level of the stand-alone performance of AI. One aspect of HCI is the impact of AI on the degree of certainty and confidence displayed by the endoscopist. Indirectly, diagnostic confidence when using AI may be linked to trust and acceptance of AI. In a BE video study, we aimed to understand the impact of AI on the diagnostic confidence of endoscopists and the possible correlation with diagnostic performance.&#13;
&#13;
Methods &#13;
22 endoscopists from 12 centers with varying levels of BE experience reviewed ninety-six standardized endoscopy videos. Endoscopists were categorized into experts and non-experts and randomly assigned to assess the videos with and without AI. Participants were randomized in two arms: Arm A assessed videos first without AI and then with AI, while Arm B assessed videos in the opposite order. Evaluators were tasked with identifying BE-related neoplasia and rating their confidence with and without AI on a scale from 0 to 9.&#13;
&#13;
Results &#13;
The utilization of AI in Arm A (without AI first, with AI second) significantly elevated confidence levels for experts and non-experts (7.1 to 8.0 and 6.1 to 6.6, respectively). Only non-experts benefitted from AI with a significant increase in accuracy (68.6% to 75.5%). Interestingly, while the confidence levels of experts without AI were higher than those of non-experts with AI, there was no significant difference in accuracy between these two groups (71.3% vs. 75.5%). In Arm B (with AI first, without AI second), experts and non-experts experienced a significant reduction in confidence (7.6 to 7.1 and 6.4 to 6.2, respectively), while maintaining consistent accuracy levels (71.8% to 71.8% and 67.5% to 67.1%, respectively).&#13;
&#13;
Conclusions &#13;
AI significantly enhanced confidence levels for both expert and non-expert endoscopists. Endoscopists felt significantly more uncertain in their assessments without AI. Furthermore, experts with or without AI consistently displayed higher confidence levels than non-experts with AI, irrespective of comparable outcomes. These findings underscore the possible role of AI in improving diagnostic confidence during endoscopic assessment.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="issn">1438-8812</identifier>
    <identifier type="doi">10.1055/s-0044-1782859</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2024, Berlin, 25.–27.04.2024</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>David Roser</author>
    <author>Michael Meinikheim</author>
    <author>Robert Mendel</author>
    <author>Christoph Palm</author>
    <author>Andreas Probst</author>
    <author>Anna Muzalyova</author>
    <author>Markus W. Scheppach</author>
    <author>Sandra Nagl</author>
    <author>Elisabeth Schnoy</author>
    <author>Christoph Römmele</author>
    <author>Dominik Andreas Helmut Otto Schulz</author>
    <author>Jakob Schlottmann</author>
    <author>Friederike Prinz</author>
    <author>David Rauber</author>
    <author>Tobias Rückert</author>
    <author>Tomoaki Matsumura</author>
    <author>G. Fernandez-Esparrach</author>
    <author>Nasim Parsa</author>
    <author>Michael F. Byrne</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>8350</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>e295</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>5</issue>
    <volume>63</volume>
    <type>conferencepresentation</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-07-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Künstliche Intelligenz als Hilfsmittel zur Detektion der Papilla duodeni major und des papillären Ostiums während der ERCP</title>
    <abstract language="deu">Einleitung &#13;
Die Endoskopische Retrograde Cholangiopankreatikographie (ERCP) ist der Goldstandard in der endoskopischen Therapie von Erkrankungen des pankreatobiliären Trakts. Allerdings ist sie technisch anspruchsvoll, schwer zu erlernen und mit einer relativ hohen Komplikationsrate assoziiert. Daher soll in der vorliegenden Machbarkeitsstudie geprüft werden, ob mithilfe eines Deeplearning- Algorithmus die Papille und das Ostium zuverlässig detektiert werden können und dieser für Endoskopiker, insbesondere in der Ausbildungssituation, ein geeignetes Hilfsmittel darstellen könnte. Material und Methodik Insgesamt wurden 1534 ERCP-Bilder von 134 Patienten analysiert, wobei sowohl die Papilla duodeni major als auch das Ostium segmentiert wurden. Anschließend erfolgte das Training eines neuronalen Netzes unter Verwendung eines Deep-Learning-Algorithmus. Für den Test des Algorithmus erfolgte eine fünffache Kreuzvalidierung.&#13;
&#13;
Ergebnisse &#13;
Auf den 1534 gelabelten Bildern wurden für die Klasse Papille ein F1-Wert von 0,7996, eine Sensitivität von 0,8488 und eine Spezifität von 0,9822 erzielt. Für die Klasse Ostium ergaben sich ein F1-Wert von 0,5198, eine Sensitivität von 0,5945 und eine Spezifität von 0,9974. Klassenübergreifend (Klasse Papille und Klasse Ostium) betrug der F1-Wert 0,6593, die Sensitivität 0,7216 und für die Spezifität 0,9898.&#13;
&#13;
Zusammenfassung &#13;
In der vorliegenden Machbarkeitsstudie zeigte das neuronale Netz eine hohe Sensitivität und eine sehr hohe Spezifität bei der Identifikation der Papilla duodeni major. Die Detektion des Ostiums erfolgte hingegen mit einer deutlich geringeren Sensitivität. Zukünftig ist eine Erweiterung des Trainingsdatensatzes um Videos und klinische Daten vorgesehen, um die Leistungsfähigkeit des Netzwerks zu verbessern. Hierdurch könnte langfristig ein geeignetes Assistenzsystem für die ERCP, insbesondere in der Ausbildungssituation etabliert werden.</abstract>
    <parentTitle language="deu">Zeitschrift für Gastroenterologie</parentTitle>
    <identifier type="doi">10.1055/s-0045-1806882</identifier>
    <enrichment key="ConferenceStatement">52. Jahrestagung der Gesellschaft für Gastroenterologie in Bayern e. V.</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Stephan Zellmer</author>
    <author>David Rauber</author>
    <author>Andreas Probst</author>
    <author>Tobias Weber</author>
    <author>Georg Braun</author>
    <author>Sandra Nagl</author>
    <author>Christoph Römmele</author>
    <author>Elisabeth Schnoy</author>
    <author>Lisa Birzle</author>
    <author>Niklas Aehling</author>
    <author>Dominik Andreas Helmut Otto Schulz</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <author>Alanna Ebigbo</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>136</id>
    <completedYear/>
    <publishedYear>2007</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>49</pageFirst>
    <pageLast>63</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>85</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Quantifying the A1AR distribution in peritumoral zones around experimental F98 and C6 rat brain tumours</title>
    <abstract language="eng">Quantification of growth in experimental F98 and C6 rat brain tumours was performed on 51 rat brains, 17 of which have been further assessed by 3D tumour reconstruction. Brains were cryosliced and radio-labelled with a ligand of the peripheral type benzodiazepine-receptor (pBR), 3H-Pk11195 [(1-(2-chlorophenyl)-N-methyl-N-(1-methyl-propylene)-3-isoquinoline-carboxamide)] by receptor autoradiography. Manually segmented and automatically registered tumours have been 3D-reconstructed for volumetric comparison on the basis of 3H-Pk11195-based tumour recognition. Furthermore automatically computed areas of −300 μm inner (marginal) zone as well as 300 μm and 600 μm outer tumour space were quantified. These three different regions were transferred onto other adjacent slices that had been labelled by receptor autoradiography with the A1 Adenosine receptor (A1AR)-ligand 3H-CPFPX (3H-8-cyclopentyl-3-(3-fluorpropyl)-1-propylxanthine) for quantitative assessment of A1AR in the three different tumour zones. Hence, a method is described for quantifying various receptor protein systems in the tumour as well as in the marginal invasive zones around experimentally implanted rat brain tumours and their representation in the tumour microenvironment as well as in 3D space. Furthermore, a tool for automatically reading out radio-labelled rat brain slices from auto radiographic films was developed, reconstructed into a consistent 3D-tumour model and the zones around the tumour were visualized. A1AR expression was found to depend upon the tumour volume in C6 animals, but is independent on the time of tumour development. In F98 animals, a significant increase in A1AR receptor protein was found in the Peritumoural zone as a function of time of tumour development and tumour volume.</abstract>
    <parentTitle language="eng">Journal of Neuro-Oncology</parentTitle>
    <identifier type="doi">10.1007/s11060-007-9391-6</identifier>
    <author>Markus Dehnhardt</author>
    <author>Christoph Palm</author>
    <author>Andrea Vieten</author>
    <author>Andreas Bauer</author>
    <author>Uwe Pietrzyk</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>3D reconstruction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>A1 adenosine receptor</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>GBM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Kmeans algorithm</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Brain tumour</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Receptor autoradiography</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Hirntumor</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Dreidimensionale Bildverarbeitung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Adenosinrezeptor</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>138</id>
    <completedYear/>
    <publishedYear>2008</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>201</pageFirst>
    <pageLast>211</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3-4</issue>
    <volume>3</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Consistency of parametric registration in serial MRI studies of brain tumor progression</title>
    <abstract language="eng">Object&#13;
The consistency of parametric registration in multi-temporal magnetic resonance (MR) imaging studies was evaluated.&#13;
Materials and methods&#13;
Serial MRI scans of adult patients with a brain tumor (glioma) were aligned by parametric registration. The performance of low-order spatial alignment (6/9/12 degrees of freedom) of different 3D serial MR-weighted images is evaluated. A registration protocol for the alignment of all images to one reference coordinate system at baseline is presented. Registration results were evaluated for both, multimodal intra-timepoint and mono-modal multi-temporal registration. The latter case might present a challenge to automatic intensity-based registration algorithms due to ill-defined correspondences. The performance of our algorithm was assessed by testing the inverse registration consistency. Four different similarity measures were evaluated to assess consistency.&#13;
Results&#13;
Careful visual inspection suggests that images are well aligned, but their consistency may be imperfect. Sub-voxel inconsistency within the brain was found for allsimilarity measures used for parametric multi-temporal registration. T1-weighted images were most reliable for establishing spatial correspondence between different timepoints.&#13;
Conclusions&#13;
The parametric registration algorithm is feasible for use in this application. The sub-voxel resolution mean displacement error of registration transformations demonstrates that the algorithm converges to an almost identical solution for forward and reverse registration.</abstract>
    <parentTitle language="eng">International Journal of Computer Assisted Radiology and Surgery</parentTitle>
    <identifier type="doi">10.1007/s11548-008-0234-5</identifier>
    <author>Andreas Mang</author>
    <author>Julia A. Schnabel</author>
    <author>William R. Crum</author>
    <author>Marc Modat</author>
    <author>Oscar Camara-Rey</author>
    <author>Christoph Palm</author>
    <author>Gisele Brasil Caseiras</author>
    <author>H. Rolf Jäger</author>
    <author>Sébastien Ourselin</author>
    <author>Thorsten M. Buzug</author>
    <author>David J. Hawkes</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Inverse registration consistency</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Parametric serial MR image registration</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Tumor disease progression</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Kernspintomografie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Registrierung &lt;Bildverarbeitung&gt;</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Hirntumor</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>139</id>
    <completedYear/>
    <publishedYear>2008</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>69140M-1</pageFirst>
    <pageLast>69140M-9</pageLast>
    <pageNumber/>
    <edition/>
    <issue>6914</issue>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Fusion of Rat Brain Histology and MRI using Weighted Multi-Image Mutual Information</title>
    <abstract language="eng">Fusion of histology and MRI is frequently demanded in biomedical research to study in vitro tissue properties in an in vivo reference space. Distortions and artifacts caused by cutting and staining of histological slices as well as differences in spatial resolution make even the rigid fusion a difficult task. State-of- the-art methods start with a mono-modal restacking yielding a histological pseudo-3D volume. The 3D information of the MRI reference is considered subsequently. However, consistency of the histology volume and consistency due to the corresponding MRI seem to be diametral goals. Therefore, we propose a novel fusion framework optimizing histology/histology and histology/MRI consistency at the same time finding a balance between both goals. Method - Direct slice-to-slice correspondence even in irregularly-spaced cutting sequences is achieved by registration-based interpolation of the MRI. Introducing a weighted multi-image mutual information metric (WI), adjacent histology and corresponding MRI are taken into account at the same time. Therefore, the reconstruction of the histological volume as well as the fusion with the MRI is done in a single step. Results - Based on two data sets with more than 110 single registrations in all, the results are evaluated quantitatively based on Tanimoto overlap measures and qualitatively showing the fused volumes. In comparison to other multi-image metrics, the reconstruction based on WI is significantly improved. We evaluated different parameter settings with emphasis on the weighting term steering the balance between intra- and inter-modality consistency.</abstract>
    <parentTitle language="eng">Proceedings of the SPIE Medical Imaging 6914: Image Processing 69140M</parentTitle>
    <identifier type="doi">10.1117/12.770605</identifier>
    <author>Christoph Palm</author>
    <author>Penny P. Graeme</author>
    <author>William R. Crum</author>
    <author>Julia A. Schnabel</author>
    <author>Uwe Pietrzyk</author>
    <author>David J. Hawkes</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Magnetic resonance imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image registration</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Brain</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>3D image processing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image fusion</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>In vitro testing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>In vivo imaging</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Kernspintomografie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Histologie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schnittdarstellung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Registrierung &lt;Bildverarbeitung&gt;</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Datenfusion</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>121</id>
    <completedYear/>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>79</pageFirst>
    <pageLast>88</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>9</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Viewpoints on Medical Image Processing</title>
    <abstract language="eng">Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment.</abstract>
    <parentTitle language="eng">Current Medical Imaging Reviews</parentTitle>
    <subTitle language="eng">From Science to Application</subTitle>
    <identifier type="doi">10.2174/1573405611309020002</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Thomas M. Deserno</author>
    <author>Heinz Handels</author>
    <author>Klaus H. Maier-Hein</author>
    <author>Sven Mersmann</author>
    <author>Christoph Palm</author>
    <author>Thomas Tolxdorff</author>
    <author>Gudrun Wagenknecht</author>
    <author>Thomas Wittenberg</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Medical imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image processing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Vizualization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multi-modal imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Diffusion-weighted imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Model-based imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital endoscopy</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildverarbeitung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Medizin</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>133</id>
    <completedYear/>
    <publishedYear>2009</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>167</pageFirst>
    <pageLast>171</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Level-Set-Segmentierung von Rattenhirn MRTs</title>
    <abstract language="deu">In dieser Arbeit wird die Segmentierung von Gehirngewebe aus Kopfaufnahmen von Ratten mittels Level-Set-Methoden vorgeschlagen. Dazu wird ein zweidimensionaler, kontrastbasierter Ansatz zu einem dreidimensionalen, lokal an die Bildintensität adaptierten Segmentierer erweitert. Es wird gezeigt, dass mit diesem echten 3D-Ansatz die lokalen Bildstrukturen besser berücksichtigt werden können. Insbesondere Magnet-Resonanz-Tomographien (MRTs) mit globalen Helligkeitsgradienten, beispielsweise bedingt durch Oberﬂächenspulen, können auf diese Weise zuverlässiger und ohne weitere Vorverarbeitungsschritte segmentiert werden. Die Leistungsfähigkeit des Algorithmus wird experimentell an Hand dreier Rattenhirn-MRTs demonstriert.</abstract>
    <parentTitle language="deu">Bildverarbeitung für die Medizin 2009; Algorithmen - Systeme - Anwendungen ; Proceedings des Workshops vom 22. bis 25. März 2009 in Heidelberg</parentTitle>
    <identifier type="url">http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-446/p167.pdf</identifier>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Björn Eiben</author>
    <author>Dietmar Kunz</author>
    <author>Uwe Pietrzyk</author>
    <author>Christoph Palm</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Dreidimensionale Bildverarbeitung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schnittdarstellung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirn</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>161</id>
    <completedYear/>
    <publishedYear>2004</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2399</pageFirst>
    <pageLast>2401</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>4</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Investigation of fusion strategies of multi-modality images</title>
    <abstract language="eng">Presenting images from different modalities seems to be a trivial task considering the challenges to obtain registered images as a pre-requisite for image fusion. In combined tomographs like PET/CT, image registration is intrinsic. However, informative image fusion mandates careful preparation owing to the large amount of information that is presented to the observer. In complex imaging situations it is required to provide tools that are easy to handle and still powerful enough to help the observer discriminating important details from background patterns. We investigated several options for color tables applied to brain and non-brain images obtained with PET, MRI and CT.</abstract>
    <parentTitle language="eng">IEEE Nuclear Science Symposium Conference Record</parentTitle>
    <identifier type="doi">10.1109/NSSMIC.2004.1462740</identifier>
    <author>Uwe Pietrzyk</author>
    <author>Christoph Palm</author>
    <author>Thomas Beyer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Positron emission tomography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Biomedical imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Medical diagnostic imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image fusion</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Computed tomography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image registration</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Visualization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Table lookup</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Humans</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>152</id>
    <completedYear/>
    <publishedYear>2006</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>284P</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>Suppl. 1</issue>
    <volume>47</volume>
    <type>conferencepresentation</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Uptake of F-18-fluoroethyl-L-tyrosine and H-3-L-methionine in focal cortical ischemia</title>
    <abstract language="eng">Objectives: C-11-methionine (MET) is particularly useful in brain tumor diagnosis but unspecific uptake e.g. in cerebral ischemia has been reported (1). The F-18-labeled amino acid O-(2-[F-18]fluoroethyl)-L-tyrosine (FET) shows a similar clinical potential as MET in brain tumor diagnosis but is applicable on a wider clinical scale. The aim of this study was to evaluate the uptake of FET and H-3-MET in focal cortical ischemia in rats by dual tracer autoradiography.&#13;
&#13;
Methods: Focal cortical ischemia was induced in 12 Fisher CDF rats using the photothrombosis model (PT). One day (n=3) , two days (n=5) and 7 days (n=4) after induction of the lesion FET and H-3-MET were injected intravenously. One hour after tracer injection animals were killed, the brains were removed immediately and frozen in 2-methylbutane at -50°C. Brains were cut in coronal sections (thickness: 20 µm) and exposed first to H-3 insensitive photoimager plates to measure FET distribution. After decay of F-18 the distribution of H-3-MET was determined. The autoradiograms were evaluated by regions of interest (ROIs) placed on areas with increased tracer uptake in the PT and the contralateral brain. Lesion to brain ratios (L/B) were calculated by dividing the mean uptake in the lesion and the brain. Based on previous studies in gliomas a L/B ratio &gt; 1.6 was considered as pathological for FET.&#13;
&#13;
Results: Variable increased uptake of both tracers was observed in the PT and its demarcation zone at all stages after PT. The cut-off level of 1.6 for FET was exceeded in 9/12 animals. One day after PT the L/B ratios were 2.0 ± 0.6 for FET vs. 2.1 ± 1.0 for MET (mean ± SD); two days after lesion 2.2 ± 0.7 for FET vs. 2.7 ± 1.0 for MET and 7 days after lesion 2.4 ± 0.4 for FET vs. 2.4 ± 0.1 for MET. In single cases discrepancies in the uptake pattern of FET and MET were observed.&#13;
&#13;
Conclusions: FET like MET may exhibit significant uptake in infarcted areas or the immediate vincinity which has to be considered in the differential diagnosis of unkown brain lesions. The discrepancies in the uptake pattern of FET and MET in some cases indicates either differences in the transport mechanisms of both amino acids or a different affinity for certain cellular components.</abstract>
    <parentTitle language="eng">The Journal of Nuclear Medicine</parentTitle>
    <identifier type="url">http://jnm.snmjournals.org/content/47/suppl_1/284P.3</identifier>
    <author>Dagmar Bauer</author>
    <author>Gabriele Stoffels</author>
    <author>Dirk Pauleit</author>
    <author>Christoph Palm</author>
    <author>Kurt Hamacher</author>
    <author>Heinz H. Coenen</author>
    <author>Karl Langen</author>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>130</id>
    <completedYear/>
    <publishedYear>2010</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>161</pageFirst>
    <pageLast>171</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>21</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Cerebral bio-imaging of Cu, Fe, Zn and Mn in the MPTP mouse model of Parkinsons disease using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS)</title>
    <abstract language="eng">Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been established as a powerful technique for the determination of metal and nonmetal distributions within biological systems with high sensitivity. An imaging LA-ICP-MS technique for Fe, Cu, Zn, and Mn was developed to produce large series of quantitative element maps in native brain sections of mice subchronically intoxicated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridin (MPTP) as a model of Parkinson’s disease. Images were calibrated using matrix-matched laboratory standards. A software solution allowing a precise delineation of anatomical structures was implemented. Coronal brain sections were analyzed crossing the striatum and the substantia nigra, respectively. Animals sacrificed 2 h, 7 d, or 28 d after the last MPTP injection and controls were investigated.&#13;
We observed significant decreases of Cu concentrations in the periventricular zone and the fascia dentata at 2 h and 7d and a recovery or overcompensation at 28 d, most pronounced in the rostral periventricular zone (+40%). In the cortex Cu decreased slightly to −10%. Fe increased in the interpeduncular nucleus (+40%) but not in the substantia nigra. This pattern is in line with a differential regulation of periventricular and parenchymal Cu, and with the histochemical localization of Fe, and congruent to regions of preferential MPTP binding described in the rodent brain.&#13;
The LA-ICP-MS technique yielded valid and statistically robust results in the present study on 39 slices from 19 animals. Our findings underline the value of routine micro-local analytical techniques in the life sciences and affirm a role of Cu availability in Parkinson’s disease.</abstract>
    <parentTitle language="eng">Journal of the American Society for Mass Spectrometry</parentTitle>
    <identifier type="doi">10.1016/j.jasms.2009.09.022</identifier>
    <author>Andreas Matusch</author>
    <author>Candan Depboylu</author>
    <author>Christoph Palm</author>
    <author>Bei Wu</author>
    <author>Günter U. Höglinger</author>
    <author>Martin K.-H. Schäfer</author>
    <author>Johanna Sabine Becker</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Inductively Couple Plasma Mass Spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Substantia Nigra</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MPTP</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser Ablation Inductively Couple Plasma Mass Spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MPTP Treatment</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>ICP-Massenspektrometrie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Metalle</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirnkarte</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>MPTP</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>124</id>
    <completedYear/>
    <publishedYear>2011</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3</pageFirst>
    <pageLast>15</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1-3</issue>
    <volume>307</volume>
    <type>article</type>
    <publisherName>eLSEVIER</publisherName>
    <publisherPlace>Elsevier</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Mass spectrometric imaging (MSI) of metals using advanced BrainMet techniques for biomedical research</title>
    <abstract language="eng">Mass spectrometric imaging (MSI) is a young innovative analytical technique and combines different fields of advanced mass spectrometry and biomedical research with the aim to provide maps of elements and molecules, complexes or fragments. Especially essential metals such as zinc, copper, iron and manganese play a functional role in signaling, metabolism and homeostasis of the cell. Due to the high degree of spatial organization of metals in biological systems their distribution analysis is of key interest in life sciences. We have developed analytical techniques termed BrainMet using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) imaging to measure the distribution of trace metals in biological tissues for biomedical research and feasibility studies—including bioaccumulation and bioavailability studies, ecological risk assessment and toxicity studies in humans and other organisms. The analytical BrainMet techniques provide quantitative images of metal distributions in brain tissue slices which can be combined with other imaging modalities such as photomicrography of native or processed tissue (histochemistry, immunostaining) and autoradiography or with in vivo techniques such as positron emission tomography or magnetic resonance tomography.&#13;
&#13;
Prospective and instrumental developments will be discussed concerning the development of the metalloprotein microscopy using a laser microdissection (LMD) apparatus for specific sample introduction into an inductively coupled plasma mass spectrometer (LMD-ICP-MS) or an application of the near field effect in LA-ICP-MS (NF-LA-ICP-MS). These nano-scale mass spectrometric techniques provide improved spatial resolution down to the single cell level.</abstract>
    <parentTitle language="eng">International Journal of Mass Spectrometry</parentTitle>
    <identifier type="doi">10.1016/j.ijms.2011.01.015</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Johanna Sabine Becker</author>
    <author>Andreas Matusch</author>
    <author>Julia Susanne Becker</author>
    <author>Bei Wu</author>
    <author>Christoph Palm</author>
    <author>Albert Johann Becker</author>
    <author>Dagmar Salber</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bioimaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Brain tissue</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser ablation inductively coupled plasma mass spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser microdissection inductively coupled plasma mass spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Metals</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Metallomics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nano-LA-ICP-MS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Tumour</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Massenspektrometrie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Metalle</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Metallproteide</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirn</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>128</id>
    <completedYear/>
    <publishedYear>2010</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>104</pageFirst>
    <pageLast>111</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume/>
    <type>article</type>
    <publisherName>Oxford Academic Press</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Bioimaging of metals in brain tissue by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and metallomics</title>
    <abstract language="eng">Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been developed and established as an emerging technique in the generation of quantitative images of metal distributions in thin tissue sections of brain samples (such as human, rat and mouse brain), with applications in research related to neurodegenerative disorders. A new analytical protocol is described which includes sample preparation by cryo-cutting of thin tissue sections and matrix-matched laboratory standards, mass spectrometric measurements, data acquisition, and quantitative analysis. Specific examples of the bioimaging of metal distributions in normal rodent brains are provided. Differences to the normal were assessed in a Parkinson’s disease and a stroke brain model. Furthermore, changes during normal aging were studied. Powerful analytical techniques are also required for the determination and characterization of metal-containing proteins within a large pool of proteins, e.g., after denaturing or non-denaturing electrophoretic separation of proteins in one-dimensional and two-dimensional gels. LA-ICP-MS can be employed to detect metalloproteins in protein bands or spots separated after gel electrophoresis. MALDI-MS can then be used to identify specific metal-containing proteins in these bands or spots. The combination of these techniques is described in the second section.</abstract>
    <parentTitle language="eng">Metallomics</parentTitle>
    <identifier type="doi">10.1039/b916722f</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Johanna Sabine Becker</author>
    <author>Andreas Matusch</author>
    <author>Christoph Palm</author>
    <author>Dagmar Salber</author>
    <author>Kathryn A. Morton</author>
    <author>Julia Susanne Becker</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>ICP-Massenspektrometrie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Metalle</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Metallproteide</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Elektrophorese</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirn</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>123</id>
    <completedYear/>
    <publishedYear>2011</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>232</pageFirst>
    <pageLast>239</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1-3</issue>
    <volume>307</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">IMAGENA: Image Generation and Analysis</title>
    <abstract language="eng">Metals are involved in many processes of life. They are needed for enzymatic reactions, are involved in healthy processes but also yield diseases if the metal homeostasis is disordered. Therefore, the interest to assess the spatial distribution of metals is rising in biomedical science. Imaging metal (and non-metal) isotopes by laser ablation mass spectrometry with inductively coupled plasma (LA-ICP-MS) requires a special software solution to process raw data obtained by scanning a sample line-by-line. As no software ready to use was available we developed an interactive software tool for Image Generation and Analysis (IMAGENA). Unless optimised for LA-ICP-MS, IMAGENA can handle other raw data as well. The general purpose was to reconstruct images from a continuous list of raw data points, to visualise these images, and to convert them into a commonly readable image file format that can be further analysed by standard image analysis software. The generation of the image starts with loading a text file that holds a data column of every measured isotope. Specifying general spatial domain settings like the data offset and the image dimensions is done by the user getting a direct feedback by means of a preview image. IMAGENA provides tools for calibration and to correct for a signal drift in the y-direction. Images are visualised in greyscale as well a pseudo-colours with possibilities for contrast enhancement. Image analysis is performed in terms of smoothed line plots in row and column direction.</abstract>
    <parentTitle language="eng">International Journal of Mass Spectrometry</parentTitle>
    <subTitle language="eng">An Interactive Software Tool handling LA-ICP-MS Data</subTitle>
    <identifier type="doi">10.1016/j.ijms.2011.03.010</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Tobias Osterholt</author>
    <author>Dagmar Salber</author>
    <author>Andreas Matusch</author>
    <author>Johanna Sabine Becker</author>
    <author>Christoph Palm</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LA-ICP-MS</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>ICP-Massenspektrometrie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bilderzeugung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Graphische Benutzeroberfläche</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image generation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Graphical user interface</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>140</id>
    <completedYear/>
    <publishedYear>2008</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>69143U-1</pageFirst>
    <pageLast>69143U-8</pageLast>
    <pageNumber/>
    <edition/>
    <issue>6914</issue>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Time-Dependent Joint Probability Speed Function for Level-Set Segmentation of Rat-Brain Slices</title>
    <abstract language="eng">The segmentation of rat brain slices suffers from illumination inhomogeneities and staining effects. State-of-the-art level-set methods model slice and background with intensity mixture densities defining the speed function as difference between the respective probabilites. Nevertheless, the overlap of these distributions causes an inaccurate stopping at the slice border. In this work, we propose the characterisation of the border area with intensity pairs for inside and outside estimating joint intensity probabilities. Method - In contrast to global object and background models, we focus on the object border characterised by a joint mixture density. This specifies the probability of the occurance of an inside and an outside value in direct adjacency. These values are not known beforehand, because inside and outside depend on the level-set evolution and change during time. Therefore, the speed function is computed time-dependently at the position of the current zero level-set. Along this zero level-set curve, the inside and outside values are derived as mean along the curvature normal directing inside and outside the object. Advantage of the joint probability distribution is to resolve the distribution overlaps, because these are assumed to be not located at the same border position. Results - The novel time-dependent joint probability based speed function is compared expermimentally with single probability based speed functions. Two rat brains with about 40 slices are segmented and the results analysed using manual segmentations and the Tanimoto overlap measure. Improved results are recognised for both data sets.</abstract>
    <parentTitle language="eng">Proceedings of the SPIE Medical Imaging 6914: Image Processing 69143U</parentTitle>
    <identifier type="doi">10.1117/12.770673</identifier>
    <author>Christoph Palm</author>
    <author>Uwe Pietrzyk</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image segmentation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Brain</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Visualization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image processing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Medical imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neuroimaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Beryllium</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Kernspintomografie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Histologie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schnittdarstellung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildsegmentierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirn</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>127</id>
    <completedYear/>
    <publishedYear>2010</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1241</pageFirst>
    <pageLast>1248</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>49</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Signal enhancement in polarized light imaging by means of independent component analysis</title>
    <abstract language="eng">Polarized light imaging (PLI) enables the evaluation of fiber orientations in histological sections of human postmortem brains, with ultra-high spatial resolution. PLI is based on the birefringent properties of the myelin sheath of nerve fibers. As a result, the polarization state of light propagating through a rotating polarimeter is changed in such a way that the detected signal at each measurement unit of a charged-coupled device (CCD) camera describes a sinusoidal signal. Vectors of the fiber orientation defined by inclination and direction angles can then directly be derived from the optical signals employing PLI analysis. However, noise, light scatter and filter inhomogeneities interfere with the original sinusoidal PLI signals. We here introduce a novel method using independent component analysis (ICA) to decompose the PLI images into statistically independent component maps. After decomposition, gray and white matter structures can clearly be distinguished from noise and other artifacts. The signal enhancement after artifact rejection is quantitatively evaluated in 134 histological whole brain sections. Thus, the primary sinusoidal signals from polarized light imaging can be effectively restored after noise and artifact rejection utilizing ICA. Our method therefore contributes to the analysis of nerve fiber orientation in the human brain within a micrometer scale.</abstract>
    <parentTitle language="eng">NeuroImage</parentTitle>
    <identifier type="doi">10.1016/j.neuroimage.2009.08.059</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Jürgen Dammers</author>
    <author>Markus Axer</author>
    <author>David Gräßel</author>
    <author>Christoph Palm</author>
    <author>Karl Zilles</author>
    <author>Katrin Amunts</author>
    <author>Uwe Pietrzyk</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Polarisiertes Licht</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Signalverarbeitung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Signaltrennung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Komponentenanalyse</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirn</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>126</id>
    <completedYear/>
    <publishedYear>2010</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>9</pageNumber>
    <edition/>
    <issue/>
    <volume>4</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards ultra-high resolution fibre tract mapping of the human brain</title>
    <abstract language="eng">Polarised light imaging (PLI) utilises the birefringence of the myelin sheaths in order to visualise the orientation of nerve fibres in microtome sections of adult human post-mortem brains at ultra-high spatial resolution. The preparation of post-mortem brains for PLI involves fixation, freezing and cutting into 100-μm-thick sections. Hence, geometrical distortions of histological sections are inevitable and have to be removed for 3D reconstruction and subsequent fibre tracking. We here present a processing pipeline for 3D reconstruction of these sections using PLI derived multimodal images of post-mortem brains. Blockface images of the brains were obtained during cutting; they serve as reference data for alignment and elimination of distortion artefacts. In addition to the spatial image transformation, fibre orientation vectors were reoriented using the transformation fields, which consider both affine and subsequent non-linear registration. The application of this registration and reorientation approach results in a smooth fibre vector field, which reflects brain morphology. PLI combined with 3D reconstruction and fibre tracking is a powerful tool for human brain mapping. It can also serve as an independent method for evaluating in vivo fibre tractography.</abstract>
    <parentTitle language="eng">Frontiers in Human Neuroscience</parentTitle>
    <subTitle language="eng">registration of polarised light images and reorientation of fibre vectors</subTitle>
    <identifier type="doi">10.3389/neuro.09.009.2010</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Christoph Palm</author>
    <author>Markus Axer</author>
    <author>David Gräßel</author>
    <author>Jürgen Dammers</author>
    <author>Johannes Lindemeyer</author>
    <author>Karl Zilles</author>
    <author>Uwe Pietrzyk</author>
    <author>Katrin Amunts</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Dreidimensionale Bildverarbeitung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Polarisiertes Licht</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirnkarte</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>134</id>
    <completedYear/>
    <publishedYear>2009</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>410</pageFirst>
    <pageLast>414</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Reduktion von Rissartefakten durch nicht-lineare Registrierung in histologischen Schnittbildern</title>
    <abstract language="deu">In dieser Arbeit wird ein Verfahren vorgestellt, das Rissartefakte, die in histologischen Rattenhirnschnitten vorkommen können, durch nicht-lineare Registrierung reduziert. Um die Optimierung in der Rissregion zu leiten, wird der Curvature Registrierungsansatz um eine Metrik basierend auf der Segmentierung der Bilder erweitert. Dabei erzielten Registrierungen mit der ausschließlichen Segmentierung des Risses bessere Ergebnisse als Registrierungen mit einer Segmentierung des gesamten Hirnschnitts. Insgesamt zeigt sich eine deutliche Verbesserung in der Rissregion, wobei der verbleibende reduzierte Riss auf die Glattheitsbedingungen des Regularisierers zurückzuführen ist.</abstract>
    <parentTitle language="deu">Bildverarbeitung für die Medizin 2009; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 22. bis 25. März 2009 in Heidelberg</parentTitle>
    <identifier type="url">http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-446/p410.pdf</identifier>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Nicole Schubert</author>
    <author>Uwe Pietrzyk</author>
    <author>Martin Reißel</author>
    <author>Christoph Palm</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Registrierung &lt;Bildverarbeitung&gt;</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Nichtlineare Optimierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildsegmentierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirn</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schnittdarstellung</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>129</id>
    <completedYear/>
    <publishedYear>2010</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>156</pageFirst>
    <pageLast>175</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>29</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Bioimaging of Metals by Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS)</title>
    <abstract language="eng">The distribution analysis of (essential, beneficial, or toxic) metals (e.g., Cu, Fe, Zn, Pb, and others), metalloids, and non‐metals in biological tissues is of key interest in life science. Over the past few years, the development and application of several imaging mass spectrometric techniques has been rapidly growing in biology and medicine. Especially, in brain research metalloproteins are in the focus of targeted therapy approaches of neurodegenerative diseases such as Alzheimer's and Parkinson's disease, or stroke, or tumor growth. Laser ablation inductively coupled plasma mass spectrometry (LA‐ICP‐MS) using double‐focusing sector field (LA‐ICP‐SFMS) or quadrupole‐based mass spectrometers (LA‐ICP‐QMS) has been successfully applied as a powerful imaging (mapping) technique to produce quantitative images of detailed regionally specific element distributions in thin tissue sections of human or rodent brain. Imaging LA‐ICP‐QMS was also applied to investigate metal distributions in plant and animal sections to study, for example, the uptake and transport of nutrient and toxic elements or environmental contamination. The combination of imaging LA‐ICP‐MS of metals with proteomic studies using biomolecular mass spectrometry identifies metal‐containing proteins and also phosphoproteins. Metal‐containing proteins were imaged in a two‐dimensional gel after electrophoretic separation of proteins (SDS or Blue Native PAGE). Recent progress in LA‐ICP‐MS imaging as a stand‐alone technique and in combination with MALDI/ESI‐MS for selected life science applications is summarized.</abstract>
    <parentTitle language="eng">Mass Spectrometry Reviews</parentTitle>
    <identifier type="doi">10.1002/mas.20239</identifier>
    <author>Johanna Sabine Becker</author>
    <author>Miroslav Zoriy</author>
    <author>Andreas Matusch</author>
    <author>Bei Wu</author>
    <author>Dagmar Salber</author>
    <author>Christoph Palm</author>
    <author>Julia Susanne Becker</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>ICP-Massenspektrometrie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Metalle</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Metallproteide</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Elektrophorese</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirnkarte</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bioimaging of metals</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser ablation inductively coupled plasma mass spectrometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>metal distribution</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>metallomics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>neurodegenerative diseases</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>162</id>
    <completedYear/>
    <publishedYear>2003</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>730</pageFirst>
    <pageLast>734</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>123</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Colour Texture Analysis for Quantitative Laryngoscopy</title>
    <abstract language="eng">Whilst considerable progress has been made in enhancing the quality of indirect laryngoscopy and image processing, the evaluation of clinical findings is still based on the clinician's judgement. The aim of this paper was to examine the feasibility of an objective computer-based method for evaluating laryngeal disease. Digitally recorded images obtained by 90 degree- and 70 degree-angled indirect rod laryngoscopy using standardized white balance values were made of 16 patients and 19 healthy subjects. The digital images were evaluated manually by the clinician based on a standardized questionnaire, and suspect lesions were marked and classified on the image. Following colour separation, normal vocal cord areas as well as suspect lesions were analyzed automatically using co-occurrence matrices, which compare colour differences between neighbouring pixels over a predefined distance. Whilst colour histograms did not provide sufficient information for distinguishing between healthy and diseased tissues, consideration of the blue content of neighbouring pixels enabled a correct classification in 81.4% of cases. If all colour channels (red, green and blue) were regarded simultaneously, the best classification correctness obtained was 77.1%. Although only a very basic classification differentiating between healthy and diseased tissue was attempted, the results showed progress compared to grey-scale histograms, which have been evaluated before. The results document a first step towards an objective, machine-based classification of laryngeal images, which could provide the basis for further development of an expert system for use in indirect laryngoscopy.</abstract>
    <parentTitle language="eng">Acta Otolaryngologica</parentTitle>
    <identifier type="doi">10.1080/00016480310000412</identifier>
    <author>Justus F. R. Ilgner</author>
    <author>Christoph Palm</author>
    <author>Andreas G. Schütz</author>
    <author>Klaus Spitzer</author>
    <author>Martin Westhofen</author>
    <author>Thomas M. Lehmann</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>diagnostic laryngoscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>electronic imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>endoscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>neoplastic larynx disease</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>132</id>
    <completedYear/>
    <publishedYear>2009</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3269</pageFirst>
    <pageLast>3289</pageLast>
    <pageNumber/>
    <edition/>
    <issue>10</issue>
    <volume>54</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Evaluation of Registration Strategies for Multi-modality Images of Rat Brain Slices</title>
    <abstract language="eng">In neuroscience, small-animal studies frequently involve dealing with series of images from multiple modalities such as histology and autoradiography. The consistent and bias-free restacking of multi-modality image series is obligatory as a starting point for subsequent non-rigid registration procedures and for quantitative comparisons with positron emission tomography (PET) and other in vivo data. Up to now, consistency between 2D slices without cross validation using an inherent 3D modality is frequently presumed to be close to the true morphology due to the smooth appearance of the contours of anatomical structures. However, in multi-modality stacks consistency is difficult to assess. In this work, consistency is defined in terms of smoothness of neighboring slices within a single modality and between different modalities. Registration bias denotes the distortion of the registered stack in comparison to the true 3D morphology and shape. Based on these metrics, different restacking strategies of multi-modality rat brain slices are experimentally evaluated. Experiments based on MRI-simulated and real dual-tracer autoradiograms reveal a clear bias of the restacked volume despite quantitatively high consistency and qualitatively smooth brain structures. However, different registration strategies yield different inter-consistency metrics. If no genuine 3D modality is available, the use of the so-called SOP (slice-order preferred) or MOSOP (modality-and-slice-order preferred) strategy is recommended.</abstract>
    <parentTitle language="eng">Physics in Medicine and Biology</parentTitle>
    <identifier type="doi">10.1088/0031-9155/54/10/021</identifier>
    <author>Christoph Palm</author>
    <author>Andrea Vieten</author>
    <author>Dagmar Salber</author>
    <author>Uwe Pietrzyk</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Histologie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schnittdarstellung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Multimodales Verfahren</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>125</id>
    <completedYear/>
    <publishedYear>2011</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1091</pageFirst>
    <pageLast>1101</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>54</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Novel Approach to the Human Connectome</title>
    <abstract language="eng">Signal transmission between different brain regions requires connecting fiber tracts, the structural basis of the human connectome. In contrast to animal brains, where a multitude of tract tracing methods can be used, magnetic resonance (MR)-based diffusion imaging is presently the only promising approach to study fiber tracts between specific human brain regions. However, this procedure has various inherent restrictions caused by its relatively low spatial resolution. Here, we introduce 3D-polarized light imaging (3D-PLI) to map the three-dimensional course of fiber tracts in the human brain with a resolution at a submillimeter scale based on a voxel size of 100 μm isotropic or less. 3D-PLI demonstrates nerve fibers by utilizing their intrinsic birefringence of myelin sheaths surrounding axons. This optical method enables the demonstration of 3D fiber orientations in serial microtome sections of entire human brains. Examples for the feasibility of this novel approach are given here. 3D-PLI enables the study of brain regions of intense fiber crossing in unprecedented detail, and provides an independent evaluation of fiber tracts derived from diffusion imaging data.</abstract>
    <parentTitle language="eng">NeuroImage</parentTitle>
    <subTitle language="eng">Ultra-High Resolution Mapping of Fiber Tracts in the Brain</subTitle>
    <identifier type="doi">10.1016/j.neuroimage.2010.08.075</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Markus Axer</author>
    <author>Katrin Amunts</author>
    <author>David Gräßel</author>
    <author>Christoph Palm</author>
    <author>Jürgen Dammers</author>
    <author>Hubertus Axer</author>
    <author>Uwe Pietrzyk</author>
    <author>Karl Zilles</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Connectome</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Human brain</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Method</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Polarized light imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Tractography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Systems biology</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Dreidimensionale Bildverarbeitung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Polarisiertes Licht</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirnkarte</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>131</id>
    <completedYear/>
    <publishedYear>2010</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>301</pageFirst>
    <pageLast>305</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Error Correction using Registration for Blockface Volume Reconstruction of Serial Histological Sections of the Human Brain</title>
    <abstract language="eng">For accurate registration of histological sections blockface images are frequently used as three dimensional reference. However, due to the use of endocentric lenses the images suﬀer from perspective errors such as scaling and seemingly relative movement of planes which are located in diﬀerent distances parallel to the imaging sensor. The suggested correction of those errors is based on the estimation of scaling factors derived from image registration of regions characterized by diﬀering distances to the point of view in neighboring sections. The correction allows the generation of a consistent three dimensional blockface volume.</abstract>
    <parentTitle language="deu">Bildverarbeitung für die Medizin 2010; Algorithmen - Systeme - Anwendungen ; Proceedings des Workshops vom 22. bis 25. März 2009 in Heidelberg</parentTitle>
    <identifier type="url">http://ceur-ws.org/Vol-574/bvm2010_61.pdf</identifier>
    <author>Björn Eiben</author>
    <author>Christoph Palm</author>
    <author>Uwe Pietrzyk</author>
    <author>Christos Davatzikos</author>
    <author>Katrin Amunts</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Histologie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Diagnostik</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schnittdarstellung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fehlerbehandlung</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>153</id>
    <completedYear/>
    <publishedYear>2006</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>384P</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>Suppl. 1</issue>
    <volume>47</volume>
    <type>conferencepresentation</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards MR-based attenuation correction for whole-body PET/MR imaging</title>
    <parentTitle language="eng">The Journal of Nuclear Medicine</parentTitle>
    <identifier type="url">http://jnm.snmjournals.org/content/47/suppl_1/384P.1.abstract</identifier>
    <author>Thomas Beyer</author>
    <author>Markus Weigert</author>
    <author>Christoph Palm</author>
    <author>Harald H. Quick</author>
    <author>Stefan P. Müller</author>
    <author>Uwe Pietrzyk</author>
    <author>Florian Vogt</author>
    <author>M.J. Martinez</author>
    <author>Andreas Bockisch</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Kernspintomografie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Positronen-Emissions-Tomografie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schwächung</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>135</id>
    <completedYear/>
    <publishedYear>2009</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>142</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>Suppl. 1</issue>
    <volume>47</volume>
    <type>conferencepresentation</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Visualization of Fiber Tracts in the Postmortem Human Brain by Means of Polarized Light</title>
    <parentTitle language="eng">NeuroImage</parentTitle>
    <identifier type="doi">10.1016/S1053-8119(09)71415-6</identifier>
    <enrichment key="ConferenceStatement">Organization for Human Brain Mapping 2009 Annual Meeting - OHBM 2009</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>David Gräßel</author>
    <author>Markus Axer</author>
    <author>Christoph Palm</author>
    <author>Jürgen Dammers</author>
    <author>Katrin Amunts</author>
    <author>Uwe Pietrzyk</author>
    <author>Karl Zilles</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirn</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Polarisiertes Licht</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Pathologische Anatomie</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>151</id>
    <completedYear/>
    <publishedYear>2007</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>A116</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>46</volume>
    <type>conferencepresentation</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Generation of a MRI reference data set for the validation of automatic, non-rigid image co-registration algorithms</title>
    <parentTitle language="deu">Nuklearmedizin</parentTitle>
    <author>Markus Weigert</author>
    <author>Thomas Beyer</author>
    <author>Harald H. Quick</author>
    <author>Uwe Pietrzyk</author>
    <author>Christoph Palm</author>
    <author>Stefan P. Müller</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Kernspintomografie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Referenzdaten</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Registrierung &lt;Bildverarbeitung&gt;</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Algorithmus</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>2012</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>14</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>135</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-06-25</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Convolutional Neural Networks for the evaluation of cancer in Barrett’s esophagus: Explainable AI to lighten up the black-box</title>
    <abstract language="eng">Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice. Hence, we aim to investigate the use of explainable artificial intelligence techniques to quantitatively highlight discriminative regions during the classification of earlycancerous tissues in Barrett’s esophagus-diagnosed patients. Four Convolutional Neural Network models (AlexNet, SqueezeNet, ResNet50, and VGG16) were analyzed using five different interpretation techniques (saliency, guided backpropagation, integrated gradients, input × gradients, and DeepLIFT) to compare their agreement with experts’ previous annotations of cancerous tissue. We could show that saliency attributes match best with the manual experts’ delineations. Moreover, there is moderate to high correlation between the sensitivity of a model and the human-and-computer agreement. The results also lightened that the higher the model’s sensitivity, the stronger the correlation of human and computational segmentation agreement. We observed a relevant relation between computational learning and experts’ insights, demonstrating how human knowledge may influence the correct computational learning.</abstract>
    <parentTitle language="eng">Computers in Biology and Medicine</parentTitle>
    <identifier type="doi">10.1016/j.compbiomed.2021.104578</identifier>
    <identifier type="issn">0010-4825</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-20126</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Luis Antonio de Souza Jr.</author>
    <author>Robert Mendel</author>
    <author>Sophia Strasser</author>
    <author>Alanna Ebigbo</author>
    <author>Andreas Probst</author>
    <author>Helmut Messmann</author>
    <author>João Paulo Papa</author>
    <author>Christoph Palm</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Computerunterstützte Medizin</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Barrett's esophagus</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Adenocarcinoma</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Explainable artificial intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Computer-aided diagnosis</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/2012/1-s2.0-S0010482521003723-main.pdf</file>
  </doc>
  <doc>
    <id>160</id>
    <completedYear/>
    <publishedYear>2004</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4001</pageFirst>
    <pageLast>4003</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>6</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Creating consistent 3D multi-modality data sets from autoradiographic and histological images of the rat brain</title>
    <abstract language="eng">Volumetric representations of autoradiographic and histological images gain ever more interest as a base to interpret data obtained with /spl mu/-imaging devices like microPET. Beyond supporting spatial orientation within rat brains especially autoradiographic images may serve as a base to quantitatively evaluate the complex uptake patterns of microPET studies with receptor ligands or tumor tracers. They may also serve for the development of rat brain atlases or data models, which can be explored during further image analysis or simulation studies. In all cases a consistent spatial representation of the rat brain, i.e. its anatomy and the corresponding quantitative uptake pattern, is required. This includes both, a restacking of the individual two-dimensional images and the exact registration of the respective volumes. We propose strategies how these volumes can be created in a consistent way and trying to limit the requirements on the circumstances during data acquisition, i.e. being independent from other sources like video imaging of the block face prior to cutting or high resolution micro-X-ray CT or micro MRI.</abstract>
    <parentTitle language="eng">IEEE Nuclear Science Symposium Conference Record</parentTitle>
    <identifier type="doi">10.1109/NSSMIC.2004.1466754</identifier>
    <author>Uwe Pietrzyk</author>
    <author>Dagmar Bauer</author>
    <author>Andrea Vieten</author>
    <author>Andreas Bauer</author>
    <author>Karl-Josef Langen</author>
    <author>Karl Zilles</author>
    <author>Christoph Palm</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neoplasms</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Brain modeling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Analytical models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Anatomy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data acquisition</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>High-resolution imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image resolution</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Computed tomography</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>155</id>
    <completedYear/>
    <publishedYear>2005</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>793</pageFirst>
    <pageLast>797</pageLast>
    <pageNumber/>
    <edition/>
    <issue>8</issue>
    <volume>32</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Preferred stereoselective brain uptake of D-serine</title>
    <abstract language="eng">Although it has long been presumed that d-amino acids are uncommon in mammalians, substantial amounts of free d-serine have been detected in the mammalian brain. d-Serine has been demonstrated to be an important modulator of glutamatergic neurotransmission and acts as an agonist at the strychnine-insensitive glycine site of N-methyl-d-aspartate receptors. The blood-to-brain transfer of d-serine is thought to be extremely low, and it is assumed that d-serine is generated by isomerization of l-serine in the brain. Stimulated by the observation of a preferred transport of the d-isomer of proline at the blood–brain barrier, we investigated the differential uptake of [3H]-d-serine and [3H]-l-serine in the rat brain 1 h after intravenous injection using quantitative autoradiography. Surprisingly, brain uptake of [3H]-d-serine was significantly higher than that of [3H]-l-serine, indicating a preferred transport of the d-enantiomer of serine at the blood–brain barrier. This finding indicates that exogenous d-serine may have a direct influence on glutamatergic neurotransmission and associated diseases.</abstract>
    <parentTitle language="eng">Nuclear Medicine and Biology</parentTitle>
    <subTitle language="eng">a modulator of glutamatergic neurotransmission</subTitle>
    <identifier type="doi">10.1016/j.nucmedbio.2005.07.004</identifier>
    <author>Dagmar Bauer</author>
    <author>Kurt Hamacher</author>
    <author>Stefan Bröer</author>
    <author>Dirk Pauleit</author>
    <author>Christoph Palm</author>
    <author>Karl Zilles</author>
    <author>Heinz H. Coenen</author>
    <author>Karl-Josef Langen</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Aminosäuren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirn</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Blut-Hirn-Schranke</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Aufnahme</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>d/l-serine</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Amino acid transport</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Blood–brain barrier</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NMDA receptors</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>167</id>
    <completedYear/>
    <publishedYear>2001</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2679</pageFirst>
    <pageLast>2691</pageLast>
    <pageNumber/>
    <edition/>
    <issue>11</issue>
    <volume>18</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Color Line Search for Illuminant Estimation in Real World Scenes</title>
    <abstract language="eng">The estimation of illuminant color is mandatory for many applications in the field of color image quantification. However, it is an unresolved problem if no additional heuristics or restrictive assumptions apply. Assuming uniformly colored and roundly shaped objects, Lee has presented a theory and a method for computing the scene-illuminant chromaticity from specular highlights [H. C. Lee, J. Opt. Soc. Am. A 3, 1694 (1986)]. However, Lee’s method, called image path search, is less robust to noise and is limited in the handling of microtextured surfaces. We introduce a novel approach to estimate the color of a single illuminant for noisy and microtextured images, which frequently occur in real-world scenes. Using dichromatic regions of different colored surfaces, our approach, named color line search, reverses Lee’s strategy of image path search. Reliable color lines are determined directly in the domain of the color diagrams by three steps. First, regions of interest are automatically detected around specular highlights, and local color diagrams are computed. Second, color lines are determined according to the dichromatic reflection model by Hough transform of the color diagrams. Third, a consistency check is applied by a corresponding path search in the image domain. Our method is evaluated on 40 natural images of fruit and vegetables. In comparison with those of Lee’s method, accuracy and stability are substantially improved. In addition, the color line search approach can easily be extended to scenes of objects with macrotextured surfaces.</abstract>
    <parentTitle language="eng">Journal of the Optical Society of America (JOSA) A</parentTitle>
    <identifier type="doi">10.1364/JOSAA.18.002679</identifier>
    <author>Thomas M. Lehmann</author>
    <author>Christoph Palm</author>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>159</id>
    <completedYear/>
    <publishedYear>2004</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>965</pageFirst>
    <pageLast>976</pageLast>
    <pageNumber/>
    <edition/>
    <issue>5</issue>
    <volume>37</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Color Texture Classification by Integrative Co-Occurrence Matrices</title>
    <abstract language="eng">Integrative Co-occurrence matrices are introduced as novel features for color texture classification. The extended Co-occurrence notation allows the comparison between integrative and parallel color texture concepts. The information profit of the new matrices is shown quantitatively using the Kolmogorov distance and by extensive classification experiments on two datasets. Applying them to the RGB and the LUV color space the combined color and intensity textures are studied and the existence of intensity independent pure color patterns is demonstrated. The results are compared with two baselines: gray-scale texture analysis and color histogram analysis. The novel features improve the classification results up to 20% and 32% for the first and second baseline, respectively.</abstract>
    <parentTitle language="eng">Pattern Recognition</parentTitle>
    <identifier type="doi">10.1016/j.patcog.2003.09.010</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Christoph Palm</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Color texture</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Co-occurrence matrix</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Integrative features</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>KolmogKorov distance</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image classification</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>154</id>
    <completedYear/>
    <publishedYear>2006</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>251</pageFirst>
    <pageLast>255</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Evaluierung von Registrierungsstrategien zur multimodalen 3D-Rekonstruktion von Rattenhirnschnitten</title>
    <abstract language="deu">In dieser Arbeit werden drei Strategien zur 3D Stapelung von multimodalen Schnittbildern vorgestellt. Die Strategien werden experimentell anhand von Dualtracer-Autoradiographien evaluiert. Dazu werden neue Maße zur Beschreibung der Konsistenz innerhalb einer Modalität und der Konsistenz der Modalitäten untereinander entwickelt, die auf bekannten Registrierungsmetriken basieren. Gerade bezüglich der Konsistenz der Modalitäten untereinander zeigen zwei Strategien die besten Resultate: (1) abwechselnde multimodale Registrierung (2) monomodale Rekonstruktion einer Modalität und multimodale 2D Registrierung der zweiten Modalität.</abstract>
    <parentTitle language="deu">Bildverarbeitung für die Medizin 2006</parentTitle>
    <identifier type="doi">10.1007/3-540-32137-3_51</identifier>
    <author>Christoph Palm</author>
    <author>Andrea Vieten</author>
    <author>Dagmar Bauer</author>
    <author>Uwe Pietrzyk</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Registrierung &lt;Bildverarbeitung&gt;</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Dreidimensionale Rekonstruktion</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirn</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schnittpräparat</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>158</id>
    <completedYear/>
    <publishedYear>2005</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1446</pageFirst>
    <pageLast>1447</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Fusion strategies in multi-modality imaging</title>
    <parentTitle language="eng">Medical Physics, Vol 2. Proceedings of the jointly held Congresses: ICMP 2005, 14th International Conference of Medical Physics of the International Organization for Medical Physics (IOMP), the European Federation of Organizations in Medical Physics (EFOMP) and the German Society of Medical Physics (DGMP) ; BMT 2005, 39th Annual Congress of the German Society for Biomedical Engineering (DGBMT) within VDE ; 14th - 17th September 2005, Nuremberg, Germany</parentTitle>
    <enrichment key="ConferenceStatement">14th International Conferece of IOMP, EFOMP and DGMP (ICMP 2005) and of 39th Annual Congress of DGBMT within VDE (BMT 2005)</enrichment>
    <enrichment key="OtherSeries">Biomedizinische Technik ; 50, Suppl. 1</enrichment>
    <author>Uwe Pietrzyk</author>
    <author>Christoph Palm</author>
    <author>Thomas Beyer</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Registrierung &lt;Bildverarbeitung&gt;</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>144</id>
    <completedYear/>
    <publishedYear>2007</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>A115</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>46</volume>
    <type>conferencepresentation</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Template for MR-based attenuation correction for whole-body PET/MR imaging</title>
    <parentTitle language="deu">Nuklearmedizin</parentTitle>
    <author>Markus Weigert</author>
    <author>Christoph Palm</author>
    <author>Harald H. Quick</author>
    <author>Stefan P. Müller</author>
    <author>Uwe Pietrzyk</author>
    <author>Thomas Beyer</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Kernspintomografie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Positronen-Emissions-Tomografie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schwächung</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>143</id>
    <completedYear/>
    <publishedYear>2007</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1569048-041</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>Suppl.</issue>
    <volume>52</volume>
    <type>conferencepresentation</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Visualization of Nerve Fibre Orientation in the Visual Cortex of the Human Brain by Means of Polarized Light</title>
    <parentTitle language="deu">Biomedizinische Technik</parentTitle>
    <author>Markus Axer</author>
    <author>Hubertus Axer</author>
    <author>Christoph Palm</author>
    <author>David Gräßel</author>
    <author>Karl Zilles</author>
    <author>Uwe Pietrzyk</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Sehrinde</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Nervenfaser</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Ausrichtung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Visualisierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Polarisiertes Licht</value>
    </subject>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>156</id>
    <completedYear/>
    <publishedYear>2005</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>597</pageFirst>
    <pageLast>598</pageLast>
    <pageNumber/>
    <edition/>
    <issue>Suppl. 1, Part 1</issue>
    <volume>50</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">3D rat brain tumor reconstruction</title>
    <parentTitle language="eng">Biomedizinische Technik</parentTitle>
    <enrichment key="ConferenceStatement">International Conference of IOMP, EFOMP and DGMP (ICMP 2005) and of 39th Annual Congress of DGBMT within VDE (BMT 2005)</enrichment>
    <author>Christoph Palm</author>
    <author>Markus Dehnhardt</author>
    <author>Andrea Vieten</author>
    <author>Uwe Pietrzyk</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Dreidimensionale Rekonstruktion</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Hirntumor</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>142</id>
    <completedYear/>
    <publishedYear>2007</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>1569048-859</pageNumber>
    <edition/>
    <issue>Suppl.</issue>
    <volume>52</volume>
    <type>conferencepresentation</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Application of Fluid and Elastic Registration Methods to Histological Rat Brain Sections</title>
    <parentTitle language="deu">Biomedizinische Technik</parentTitle>
    <author>Christoph Palm</author>
    <author>William R. Crum</author>
    <author>Uwe Pietrzyk</author>
    <author>David J. Hawkes</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Registrierung &lt;Bildverarbeitung&gt;</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirn</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schnittdarstellung</value>
    </subject>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>350</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>66</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>2019</volume>
    <type>article</type>
    <publisherName>Oxford University Pres</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-04-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Large expert-curated database for benchmarking document similarity detection in biomedical literature search</title>
    <abstract language="eng">Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.</abstract>
    <parentTitle language="eng">Database</parentTitle>
    <identifier type="doi">10.1093/database/baz085</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY-SA - Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International</licence>
    <author>Peter Brown</author>
    <author>RELISH Consortium</author>
    <author>Yaoqi Zhou</author>
    <author>Christoph Palm</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Information Retrieval</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Indexierung &lt;Inhaltserschließung&gt;</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Literaturdatenbank</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Dokument</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Ähnlichkeitssuche</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Suchmaschine</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>352</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>6</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>04</issue>
    <volume>51</volume>
    <type>conferencepresentation</type>
    <publisherName>Georg Thieme Verlag</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-04-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Artificial Intelligence in Early Barrett's Cancer: The Segmentation Task</title>
    <abstract language="eng">Aims:&#13;
The delineation of outer margins of early Barrett's cancer can be challenging even for experienced endoscopists. Artificial intelligence (AI) could assist endoscopists faced with this task. As of date, there is very limited experience in this domain. In this study, we demonstrate the measure of overlap (Dice coefficient = D) between highly experienced Barrett endoscopists and an AI system in the delineation of cancer margins (segmentation task).&#13;
&#13;
Methods:&#13;
An AI system with a deep convolutional neural network (CNN) was trained and tested on high-definition endoscopic images of early Barrett's cancer (n = 33) and normal Barrett's mucosa (n = 41). The reference standard for the segmentation task were the manual delineations of tumor margins by three highly experienced Barrett endoscopists. Training of the AI system included patch generation, patch augmentation and adjustment of the CNN weights. Then, the segmentation results from patch classification and thresholding of the class probabilities. Segmentation results were evaluated using the Dice coefficient (D).&#13;
&#13;
Results:&#13;
The Dice coefficient (D) which can range between 0 (no overlap) and 1 (complete overlap) was computed only for images correctly classified by the AI-system as cancerous. At a threshold of t = 0.5, a mean value of D = 0.72 was computed.&#13;
&#13;
Conclusions:&#13;
AI with CNN performed reasonably well in the segmentation of the tumor region in Barrett's cancer, at least when compared with expert Barrett's endoscopists. AI holds a lot of promise as a tool for better visualization of tumor margins but may need further improvement and enhancement especially in real-time settings.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/s-0039-1681187</identifier>
    <enrichment key="ConferenceStatement">ESGE Days 2019</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Alanna Ebigbo</author>
    <author>Robert Mendel</author>
    <author>Andreas Probst</author>
    <author>Johannes Manzeneder</author>
    <author>Luis Antonio de Souza Jr.</author>
    <author>João Paulo Papa</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Speiseröhrenkrankheit</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Barrett's esphagus</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Segmentation</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>169</id>
    <completedYear/>
    <publishedYear>2001</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automated Analysis of Stroboscopic Image Sequences by Vibration Profiles</title>
    <abstract language="eng">A method for automated segmentation of vocal cords in stroboscopic video sequences is presented.&#13;
In contrast to earlier approaches, the inner and outer contours of the vocal cords are independently delineated. Automatic segmentation of the low contrasted images is carried out by connecting the shape constraint of a point distribution model to a multi-channel regionbased balloon model. This enables us to robustly compute a vibration profile that is used as a new diagnostic tool to visualize several vibration parameters in only one graphic. The vibration profiles are studied in two cases: one physiological vibration and one functional pathology.</abstract>
    <parentTitle language="eng">Advances in Quantitative Laryngoscopy, Voice and Speech Research, Procs. 5th International Workshop</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/242439073_Automated_Analysis_of_Stroboscopic_Image_Sequences_by_Vibration_Profiles</identifier>
    <author>Christoph Palm</author>
    <author>Thomas M. Lehmann</author>
    <author>J. Bredno</author>
    <author>C. Neuschaefer-Rube</author>
    <author>S. Klajman</author>
    <author>Klaus Spitzer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Vibration Profile</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Stroboscopic Images</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Contour Detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Balloon Model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Point Distribution Model</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>172</id>
    <completedYear/>
    <publishedYear>2000</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>106</pageFirst>
    <pageLast>110</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Hierarchische Wasserscheiden-Transformation zur Lippensegmentierung in Farbbildern</title>
    <abstract language="deu">Zur Lösung komplexer Segmentierungsprobleme wird eine hierarchische und farbbasierte Wasserscheidentransformation vorgestellt. Geringe Modifikationen bezüglich Startpunktwahl und Flutungsprozess resultieren in signifikanten Verbesserungen der Segmentierung. Das Verfahren wurde zur Lippendetektion in Farbbildsequenzen eingesetzt, die zur quantitativen Beschreibung von Sprechbewegungsabläufen automatisch ausgewertet werden. Die Experimente mit 245 Bildern aus 6 Sequenzen zeigten eine Fehlerrate von 13%.</abstract>
    <parentTitle language="deu">Bildverarbeitung für die Medizin 2000</parentTitle>
    <identifier type="doi">10.1007/978-3-642-59757-2_20</identifier>
    <author>Christoph Palm</author>
    <author>B. Fischer</author>
    <author>Thomas M. Lehmann</author>
    <author>Klaus Spitzer</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Hierarchische Wasserscheiden-Transformation</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Segmentierung der Lippen</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Bewegungsanalyse</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Farbbildverarbeitung</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>171</id>
    <completedYear/>
    <publishedYear>2000</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>49</pageFirst>
    <pageLast>56</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Color Texture Analysis of Moving Vocal Cords Using Approaches from Statistics and Signal Theory</title>
    <abstract language="eng">Textural features are applied for detection of morphological pathologies of vocal cords. Cooccurrence matrices as statistical features are presented as well as filter bank analysis by Gabor filters. Both methods are extended to handle color images. Their robustness against camera movement and vibration of vocal cords is evaluated. Classification results due to three in vivo sequences are in between 94.4 % and 98.9%. The classification errors decrease if color features are used instead of grayscale features for both statistical and Fourier features</abstract>
    <parentTitle language="eng">Advances in Quantitative Laryngoscopy, Voice and Speech Research, Procs. 4th International Workshop, Friedrich Schiller University, Jena</parentTitle>
    <author>Christoph Palm</author>
    <author>Thomas M. Lehmann</author>
    <author>Klaus Spitzer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Color Texture</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gabor Filter</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Cooccurrence Matrix</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image Processing</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>168</id>
    <completedYear/>
    <publishedYear>2001</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>58</pageFirst>
    <pageLast>61</pageLast>
    <pageNumber/>
    <edition/>
    <issue>9</issue>
    <volume>2001/2002</volume>
    <type>article</type>
    <publisherName>Median</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">3D-Visualisierung glottaler Abduktionsbewegungen</title>
    <parentTitle language="deu">Aktuelle phoniatrisch-pädaudiologische Aspekte</parentTitle>
    <identifier type="isbn">3-922766-76-5</identifier>
    <author>C. Neuschaefer-Rube</author>
    <author>Thomas M. Lehmann</author>
    <author>Christoph Palm</author>
    <author>J. Bredno</author>
    <author>S. Klajman</author>
    <author>Klaus Spitzer</author>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>166</id>
    <completedYear/>
    <publishedYear>2002</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>238</pageFirst>
    <pageLast>241</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Selektion von Farbtexturmerkmalen zur Tumorklassifikation dermatoskopischer Fotografien</title>
    <parentTitle language="deu">Bildverarbeitung für die Medizin 2002</parentTitle>
    <author>B. Fischer</author>
    <author>Christoph Palm</author>
    <author>Thomas M. Lehmann</author>
    <author>Klaus Spitzer</author>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>163</id>
    <completedYear/>
    <publishedYear>2003</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>book</type>
    <publisherName>Der Andere Verlag</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Integrative Auswertung von Farbe und Textur</title>
    <identifier type="url">http://publications.rwth-aachen.de/record/58707/files/Palm_Christoph.pdf</identifier>
    <author>Christoph Palm</author>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>157</id>
    <completedYear/>
    <publishedYear>2005</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>R103</issue>
    <volume>371</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">3D rat brain tumors</title>
    <parentTitle language="eng">Naunyn-Schmiedebergs Archives of Pharmacology</parentTitle>
    <author>Christoph Palm</author>
    <author>Markus Dehnhardt</author>
    <author>Andrea Vieten</author>
    <author>Uwe Pietrzyk</author>
    <author>Andreas Bauer</author>
    <author>Karl Zilles</author>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>419</id>
    <completedYear/>
    <publishedYear>1998</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>300</pageFirst>
    <pageLast>303</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>MMV Medien und Medizin</publisherName>
    <publisherPlace>München</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Nutzung eines Farbkonstanz-Algorithmus zur Entfernung von Glanzlichtern in laryngoskopischen Bildern</title>
    <abstract language="deu">1 Einführung &#13;
Funktionelle und organische Störungen im Larynx beeinträchtigen die Ausdrucksfähigkeit des Menschen. Zur Diagnostik und Verlaufkontrolle werden die Stimmlippen im Larynx mit Hilfe der Video-Laryngoskopie aufgenommen. Zur optimalen Farbmessung wird dazu an das Lupenendoskop eine 3-Chip-CCD-Kamera angeschlossen, die eine unabhängige Aufnahme der drei Farbkanäle erlaubt. Die bisherige subjektive Befundung ist von der Erfahrung des Untersuchers abhängig und läßt nur eine grobe Klassifikation der Krankheitsbilder zu. Zur Objektivierung werden daher quantitative Parameter für Farbe, Textur und Schwingung entwickelt. Neben dem Einfluß der wechselnden Lichtquellenfarbe auf den Farbeindruck ist die Sekretauflage auf den Stimmlippen ein Problem bei der Farb-und Texturanalyse. Sie kann zu ausgedehnten Glanzlichtern führen und so weite Bereiche der Stimmlippen für die Farb-und Texturanalyse unbrauchbar machen. Dieser Beitrag stellt einen Farbkonstanz-Algorithmus vor, der unabhängig von der Lichtquelle quantitative Farbwerte des Gewebes liefert und die Glanzlichtdetektion und -elimination ermöglicht. &#13;
2 Methodik &#13;
Ziel des Farbkonstanz-Algorithmus ist die Trennung von Lichtquellen-und Gewebefarbe. Unter Verwendung des dichromatischen Reflexionsmodells [1] kann die Oberflächenreflexion mit der Farbe der Lichtquelle und die Körperreflexion mit der Gewebefarbe identifiziert werden. Der Farbeindruck entsteht aus der Linearkombination beider Farbkomponenten. Ihre Gewichtung ist von der Aufnahmegeometrie abhängig, insbesondere vom Winkel zwischen Oberflächennormalen und dem Positionsvektor der Lichtquelle. In einem zweistufigen Verfahren wird zunächst die Lichtquellenfarbe geschätzt, dann die Gewebefarbe ermittelt. Hieraus können beide Farbanteile durch die Berechnung der Gewichtsfaktoren pixelweise getrennt werden.</abstract>
    <parentTitle language="deu">Methoden der Medizinischen Informatik, Biometrie und Epidemiologie in der modernen Informationsgesellschaft</parentTitle>
    <identifier type="isbn">9783820813357</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Christoph Palm</author>
    <author>Ingrid Scholl</author>
    <author>Thomas M. Lehmann</author>
    <author>Klaus Spitzer</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Farbkonstanz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Glanzlichtelimination</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>medizinische Bildverarbeitung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>dichromatisches Reflexionsmodell</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>348</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>75</pageFirst>
    <pageLast>80</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer Vieweg</publisherName>
    <publisherPlace>Wiesbaden</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-04-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Semantic Lung Segmentation Using Convolutional Neural Networks</title>
    <abstract language="eng">Chest X-Ray (CXR) images as part of a non-invasive diagnosis method are commonly used in today’s medical workflow. In traditional methods, physicians usually use their experience to interpret CXR images, however, there is a large interobserver variance. Computer vision may be used as a standard for assisted diagnosis. In this study, we applied an encoder-decoder neural network architecture for automatic lung region detection. We compared a three-class approach (left lung, right lung, background) and a two-class approach (lung, background). The differentiation of left and right lungs as direct result of a semantic segmentation on basis of neural nets rather than post-processing a lung-background segmentation is done here for the first time. Our evaluation was done on the NIH Chest X-ray dataset, from which 1736 images were extracted and manually annotated. We achieved 94:9% mIoU and 92% mIoU as segmentation quality measures for the two-class-model and the three-class-model, respectively. This result is very promising for the segmentation of lung regions having the simultaneous classification of left and right lung in mind.</abstract>
    <parentTitle language="deu">Bildverarbeitung für die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2020 in Berlin</parentTitle>
    <identifier type="isbn">978-3-658-29266-9</identifier>
    <identifier type="doi">10.1007/978-3-658-29267-6_17</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Ching-Sheng Chang</author>
    <author>Jin-Fa Lin</author>
    <author>Ming-Ching Lee</author>
    <author>Christoph Palm</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Neuronales Netz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Segmentierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Brustkorb</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Encoder-Decoder Network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Chest X-Ray</value>
    </subject>
    <collection role="ddc" number="616">Krankheiten</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>356</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>166</pageFirst>
    <pageLast>173</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Barrett's Esophagus Identification Using Color Co-occurrence Matrices</title>
    <abstract language="eng">In this work, we propose the use of single channel Color Co-occurrence Matrices for texture description of Barrett’sEsophagus (BE)and adenocarcinoma images. Further classification using supervised  learning techniques, such as Optimum-Path Forest (OPF), Support  Vector Machines with Radial Basisunction (SVM-RBF) and Bayesian classifier supports the contextof automatic BE and adenocarcinoma  diagnosis. We validated three approaches of classification based on  patches, patients and images in two datasets (MICCAI 2015 and  Augsburg) using the color-and-texture descriptors and the machine learning techniques. Concerning MICCAI 2015 dataset, the best results were obtained using the blue channel for the descriptors and the  supervised OPF for classification purposes in the patch-based   approach,  with sensitivity nearly to 73% for positive adenocarcinoma  identification and specificity close to 77% for BE (non-cancerous) patch classification. Regarding the Augsburg dataset, the most   accurate results were also obtained using both OPF classifier and  blue channel descriptor for the feature extraction, with sensitivity close to 67% and specificity around to76%. Our work highlights new advances in the related research area and provides a promising  technique that combines color and texture information, allied to  three different approaches of dataset pre-processing aiming to  configure robust scenarios for the classification step.</abstract>
    <parentTitle language="eng">31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, 2018</parentTitle>
    <identifier type="doi">10.1109/SIBGRAPI.2018.00028</identifier>
    <author>Luis Antonio de Souza Jr.</author>
    <author>Alanna Ebigbo</author>
    <author>Andreas Probst</author>
    <author>Helmut Messmann</author>
    <author>João Paulo Papa</author>
    <author>Robert Mendel</author>
    <author>Christoph Palm</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Barrett’s  Esophagus</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Co-occurrence  Matrices</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Texture Analysis</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>357</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>125</pageFirst>
    <pageLast>134</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-04-30</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Synthesis of Medical Images Using GANs</title>
    <abstract language="eng">The success of artificial intelligence in medicine is based on the need for large amounts of high quality training data. Sharing of medical image data, however, is often restricted by laws such as doctor-patient confidentiality. Although there are publicly available medical datasets, their quality and quantity are often low. Moreover, datasets are often imbalanced and only represent a fraction of the images generated in hospitals or clinics and can thus usually only be used as training data for specific problems. The introduction of generative adversarial networks (GANs) provides a mean to generate artificial images by training two convolutional networks. This paper proposes a method which uses GANs trained on medical images in order to generate a large number of artificial images that could be used to train other artificial intelligence algorithms. This work is a first step towards alleviating data privacy concerns and being able to publicly share data that still contains a substantial amount of the information in the original private data. The method has been evaluated on several public datasets and quantitative and qualitative tests showing promising results.</abstract>
    <parentTitle language="eng">Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures. First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019</parentTitle>
    <identifier type="isbn">978-3-030-32688-3</identifier>
    <identifier type="issn">0302-9743</identifier>
    <identifier type="doi">10.1007/978-3-030-32689-0_13</identifier>
    <enrichment key="ConferenceStatement">CLIP: Workshop on Clinical Image-Based Procedures UNSURE: International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Luise Middel</author>
    <author>Christoph Palm</author>
    <author>Marius Erdt</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Neuronale Netze</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Generative adversarial networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data privacy</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bilderzeugung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Datenschutz</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>414</id>
    <completedYear/>
    <publishedYear>1999</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>3</pageFirst>
    <pageLast>10</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation>Zentrum für Bild- und Signalverarbeitung e. V. Ilmenau</contributingCorporation>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Bestimmung der Lichtquellenfarbe bei der Endoskopie mikrotexturierter Oberflächen des Kehlkopfes</title>
    <abstract language="deu">Zur Unterstützung der Diagnose von Stimmlippenerkrankungen werden innerhalb des Forschungsprojektes Quantitative Digitale Laryngoskopie  objektive Parameter zur Beschreibung der Bewegung, der Farbe sowie der Form der Stimmlippen entwickelt und klinisch evaluiert. Während die Bewegungsanalyse Aufschluß über funktionelle Stimmstörungen gibt, beschreiben Parameter der Farb- und Formanalyse morphologische Veränderungen des Stimmlippengewebes. In diesem Beitrag werden die Methoden und bisherigen Ergebnisse zur Bewegungs- und Farbanalyse vorgestellt.&#13;
Die Bewegungsanalyse wurde mit einem erweiterten Konturmodell (Snakes) durchgeführt. Aufgrund des modifizierten Konturmodells konnten die Konturen der Stimmlippen automatisch über die gesmate Bildsequenz zuverlässig detektiert werden. Die Vermssung der Konturen liefert neue quantitative Parameter zur Befundung von laryngoskopischen Stimmlippenaufnahmen.&#13;
Um die Farbeigenschaften der Stimmlippen zu bestimmen, wurde ausgehend vom RGB-Bild die Objektfarbe unabhängig von der Farbe der Lichtquelle durch Verwendung von Clusterverfahren und der Viertelkreisanalyse berechnet. Mit dieser Farbanalyse konnte die Farbe der Lichtquelle ermittelt und das beleuchtungsunabhängige Farbbild berechnet werden. Die Quanitifizierung der Rötung der Stimmlippen ist z.B. ein entscheidendes Kriterium zur Diagnostik der akuten Laryngitis.</abstract>
    <parentTitle language="deu">5. Workshop Farbbildverarbeitung, Ilmenau, 1999</parentTitle>
    <identifier type="url">http://www.germancolorgroup.de/html/Vortr_99_pdf/01_Palm.pdf</identifier>
    <author>Christoph Palm</author>
    <author>Thomas M. Lehmann</author>
    <author>Klaus Spitzer</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Konturverfolgung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Snakes</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Dichromatisches Reflexionsmodell</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Farbkonstanz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Laryngoskopie</value>
    </subject>
    <collection role="ddc" number="621">Angewandte Physik</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>347</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>14</pageFirst>
    <pageLast>19</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer Vieweg</publisherName>
    <publisherPlace>Wiesbaden</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-04-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Retrospective Color Shading Correction for Endoscopic Images</title>
    <abstract language="eng">In this paper, we address the problem of retrospective color shading correction. An extension of the established gray-level shading correction algorithm based on signal envelope (SE) estimation to color images is developed using principal color components. Compared to the probably most general shading correction algorithm based on entropy minimization, SE estimation does not need any computationally expensive optimization and thus can be implemented more effciently. We tested our new shading correction scheme on artificial as well as real endoscopic images and observed promising results. Additionally, an indepth analysis of the stop criterion used in the SE estimation algorithm is provided leading to the conclusion that a fixed, user-defined threshold is generally not feasible. Thus, we present new ideas how to develop a non-parametric version of the SE estimation algorithm using entropy.</abstract>
    <parentTitle language="deu">Bildverarbeitung für die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2020 in Berlin</parentTitle>
    <identifier type="isbn">978-3-658-29266-9</identifier>
    <identifier type="doi">10.1007/978-3-658-29267-6</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Maximilian Weiherer</author>
    <author>Martin Zorn</author>
    <author>Thomas Wittenberg</author>
    <author>Christoph Palm</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Endoskopie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Farbenraum</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Graustufe</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>413</id>
    <completedYear/>
    <publishedYear>2000</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>45</pageFirst>
    <pageLast>49</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification</title>
    <abstract language="eng">Objective:&#13;
Complex hue/saturation images as a new approach for color texture  classification using Gabor filters are introduced and compared with common techniques.&#13;
&#13;
Method:&#13;
The interpretation of hue and saturationas polar coordinates allows  direct use of the HSV-colorspace for Fourier transform. This technique is applied for Gabor feature extraction of color textures. In contrast to other color features based on the RGB-colorspace [1] the combination of color bands is done previous to the filtering.&#13;
&#13;
Results:&#13;
The performance of the new HS-featuresis compared with that of RGB  based as well as grayscale Gabor features by evaluating the classifi-cation of 30 natural textures. The new HS-featuresshow same results like the best RGB features but allow a more compact representation.  On the averagethe color features improve the results of  grayscale features.&#13;
&#13;
Conclusion:&#13;
The consideration of the color information enhances the  classification of color texture. The choice of colorspace cannot be adjudged finally, but the introduced features suggest the use of  the HSV-colorspace with less features than RGB.</abstract>
    <parentTitle language="eng">Proceedings of the 5th Joint Conference on Information Science (JCIS) 2, The Association for Intelligent Machinery, Atlantic City, NJ, 2000</parentTitle>
    <identifier type="url">http://www.keysers.net/daniel/files/JCIS2000_palm.pdf</identifier>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Christoph Palm</author>
    <author>Daniel Keysers</author>
    <author>Thomas M. Lehmann</author>
    <author>Klaus Spitzer</author>
    <collection role="ddc" number="006">Spezielle Computerverfahren</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>353</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>615</pageFirst>
    <pageLast>616</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>69</volume>
    <type>article</type>
    <publisherName>BMJ</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-04-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Real-time use of artificial intelligence in the evaluation of cancer in Barrett’s oesophagus</title>
    <abstract language="eng">Based on previous work by our group with manual annotation of visible Barrett oesophagus (BE) cancer images, a real-time deep learning artificial intelligence (AI) system was developed. While an expert endoscopist conducts the endoscopic assessment of BE, our AI system captures random images from the real-time camera livestream and provides a global prediction (classification), as well as a dense prediction (segmentation) differentiating accurately between normal BE and early oesophageal adenocarcinoma (EAC). The AI system showed an accuracy of 89.9% on 14 cases with neoplastic BE.</abstract>
    <parentTitle language="eng">Gut</parentTitle>
    <identifier type="doi">10.1136/gutjnl-2019-319460</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Alanna Ebigbo</author>
    <author>Robert Mendel</author>
    <author>Andreas Probst</author>
    <author>Johannes Manzeneder</author>
    <author>Friederike Prinz</author>
    <author>Luis Antonio de Souza Jr.</author>
    <author>João Paulo Papa</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Speiseröhrenkrankheit</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Diagnose</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Barrett's esophagus</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>real-time</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>165</id>
    <completedYear/>
    <publishedYear>2002</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>195</pageFirst>
    <pageLast>219</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2/3</issue>
    <volume>11</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Classification of Color Textures by Gabor Filtering</title>
    <parentTitle language="eng">Machine GRAPHICS &amp; VISION</parentTitle>
    <author>Christoph Palm</author>
    <author>Thomas M. Lehmann</author>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>672</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1458</pageFirst>
    <pageLast>1468</pageLast>
    <pageNumber/>
    <edition/>
    <issue>8</issue>
    <volume>70</volume>
    <type>article</type>
    <publisherName>BMJ</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis</title>
    <abstract language="eng">Objective: Artificial intelligence (AI) may reduce underdiagnosed or overlooked upper GI (UGI) neoplastic and preneoplastic conditions, due to subtle appearance and low disease prevalence. Only disease-specific AI performances have been reported, generating uncertainty on its clinical value.&#13;
&#13;
Design: We searched PubMed, Embase and Scopus until July 2020, for studies on the diagnostic performance of AI in detection and characterisation of UGI lesions. Primary outcomes were pooled diagnostic accuracy, sensitivity and specificity of AI. Secondary outcomes were pooled positive (PPV) and negative (NPV) predictive values. We calculated pooled proportion rates (%), designed summary receiving operating characteristic curves with respective area under the curves (AUCs) and performed metaregression and sensitivity analysis.&#13;
&#13;
Results: Overall, 19 studies on detection of oesophageal squamous cell neoplasia (ESCN) or Barrett's esophagus-related neoplasia (BERN) or gastric adenocarcinoma (GCA) were included with 218, 445, 453 patients and 7976, 2340, 13 562 images, respectively. AI-sensitivity/specificity/PPV/NPV/positive likelihood ratio/negative likelihood ratio for UGI neoplasia detection were 90% (CI 85% to 94%)/89% (CI 85% to 92%)/87% (CI 83% to 91%)/91% (CI 87% to 94%)/8.2 (CI 5.7 to 11.7)/0.111 (CI 0.071 to 0.175), respectively, with an overall AUC of 0.95 (CI 0.93 to 0.97). No difference in AI performance across ESCN, BERN and GCA was found, AUC being 0.94 (CI 0.52 to 0.99), 0.96 (CI 0.95 to 0.98), 0.93 (CI 0.83 to 0.99), respectively. Overall, study quality was low, with high risk of selection bias. No significant publication bias was found.&#13;
&#13;
Conclusion: We found a high overall AI accuracy for the diagnosis of any neoplastic lesion of the UGI tract that was independent of the underlying condition. This may be expected to substantially reduce the miss rate of precancerous lesions and early cancer when implemented in clinical practice.</abstract>
    <parentTitle language="eng">Gut</parentTitle>
    <identifier type="doi">10.1136/gutjnl-2020-321922</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Julia Arribas</author>
    <author>Giulio Antonelli</author>
    <author>Leonardo Frazzoni</author>
    <author>Lorenzo Fuccio</author>
    <author>Alanna Ebigbo</author>
    <author>Fons van der Sommen</author>
    <author>Noha Ghatwary</author>
    <author>Christoph Palm</author>
    <author>Miguel Coimbra</author>
    <author>Francesco Renna</author>
    <author>Jacques J.G.H.M. Bergman</author>
    <author>Prateek Sharma</author>
    <author>Helmut Messmann</author>
    <author>Cesare Hassan</author>
    <author>Mario J. Dinis-Ribeiro</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>680</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>878</pageFirst>
    <pageLast>883</pageLast>
    <pageNumber/>
    <edition/>
    <issue>09</issue>
    <volume>53</volume>
    <type>article</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace>Stuttgart</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-11-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study</title>
    <abstract language="eng">Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. &#13;
&#13;
Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. &#13;
&#13;
Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. &#13;
&#13;
Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.</abstract>
    <parentTitle language="eng">Endoscopy</parentTitle>
    <identifier type="doi">10.1055/a-1311-8570</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Alanna Ebigbo</author>
    <author>Robert Mendel</author>
    <author>Tobias Rückert</author>
    <author>Laurin Schuster</author>
    <author>Andreas Probst</author>
    <author>Johannes Manzeneder</author>
    <author>Friederike Prinz</author>
    <author>Matthias Mende</author>
    <author>Ingo Steinbrück</author>
    <author>Siegbert Faiss</author>
    <author>David Rauber</author>
    <author>Luis Antonio de Souza Jr.</author>
    <author>João Paulo Papa</author>
    <author>Pierre Deprez</author>
    <author>Tsuneo Oyama</author>
    <author>Akiko Takahashi</author>
    <author>Stefan Seewald</author>
    <author>Prateek Sharma</author>
    <author>Michael F. Byrne</author>
    <author>Christoph Palm</author>
    <author>Helmut Messmann</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Neuronales Netz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Speiseröhrenkrebs</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Diagnose</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Adenocarcinoma</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Barrett’s cancer</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>submucosal invasion</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>662</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>12</pageNumber>
    <edition/>
    <issue>November</issue>
    <volume>126</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-10-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Assisting Barrett's esophagus identification using endoscopic data augmentation based on Generative Adversarial Networks</title>
    <abstract language="eng">Barrett's esophagus figured a swift rise in the number of cases in the past years. Although traditional diagnosis methods offered a vital role in early-stage treatment, they are generally time- and resource-consuming. In this context, computer-aided approaches for automatic diagnosis emerged in the literature since early detection is intrinsically related to remission probabilities. However, they still suffer from drawbacks because of the lack of available data for machine learning purposes, thus implying reduced recognition rates. This work introduces Generative Adversarial Networks to generate high-quality endoscopic images, thereby identifying Barrett's esophagus and adenocarcinoma more precisely. Further, Convolution Neural Networks are used for feature extraction and classification purposes. The proposed approach is validated over two datasets of endoscopic images, with the experiments conducted over the full and patch-split images. The application of Deep Convolutional Generative Adversarial Networks for the data augmentation step and LeNet-5 and AlexNet for the classification step allowed us to validate the proposed methodology over an extensive set of datasets (based on original and augmented sets), reaching results of 90% of accuracy for the patch-based approach and 85% for the image-based approach. Both results are based on augmented datasets and are statistically different from the ones obtained in the original datasets of the same kind. Moreover, the impact of data augmentation was evaluated in the context of image description and classification, and the results obtained using synthetic images outperformed the ones over the original datasets, as well as other recent approaches from the literature. Such results suggest promising insights related to the importance of proper data for the accurate classification concerning computer-assisted Barrett's esophagus and adenocarcinoma detection.</abstract>
    <parentTitle language="eng">Computers in Biology and Medicine</parentTitle>
    <identifier type="doi">10.1016/j.compbiomed.2020.104029</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Luis Antonio de Souza Jr.</author>
    <author>Leandro A. Passos</author>
    <author>Robert Mendel</author>
    <author>Alanna Ebigbo</author>
    <author>Andreas Probst</author>
    <author>Helmut Messmann</author>
    <author>Christoph Palm</author>
    <author>João Paulo Papa</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Barrett's esophagus</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Adenocarcinoma</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Generative adversarial networks</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Neuronales Netz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Adenocarcinom</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Speiseröhrenkrebs</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Diagnose</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>2023</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>preprint</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-07-30</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Learning the shape of female breasts: an open-access 3D statistical shape model of the female breast built from 110 breast scans</title>
    <abstract language="eng">We present the Regensburg Breast Shape Model (RBSM) – a 3D statistical shape model of the female breast built from 110 breast scans, and the ﬁrst ever publicly available. Together with the model, a fully automated, pairwise surface registration pipeline used to establish correspondence among 3D breast scans is introduced. Our method is computationally eﬃcient and requires only four landmarks to guide the registration process. In order to weaken the strong coupling between breast and thorax, we propose to minimize the variance outside the breast region as much as possible. To achieve this goal, a novel concept called breast probability masks (BPMs) is introduced. A BPM assigns probabilities to each point of a 3D breast scan, telling how likely it is that a particular point belongs to the breast area. During registration, we use BPMs to align the template to the target as accurately as possible inside the breast region and only roughly outside. This simple yet eﬀective strategy signiﬁcantly reduces the unwanted variance outside the breast region, leading to better statistical shape models in which breast shapes are quite well decoupled from the thorax. The RBSM is thus able to produce a variety of diﬀerent breast shapes as independently as possible from the shape of the thorax. Our systematic experimental evaluation reveals a generalization ability of 0.17 mm and a speciﬁcity of 2.8 mm for the RBSM. Ultimately, our model is seen as a ﬁrst step towards combining physically motivated deformable models of the breast and statistical approaches in order to enable more realistic surgical outcome simulation.</abstract>
    <identifier type="arxiv">2107.13463</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <author>Maximilian Weiherer</author>
    <author>Andreas Eigenberger</author>
    <author>Vanessa Brébant</author>
    <author>Lukas Prantl</author>
    <author>Christoph Palm</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Statistical shape mode</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Surgical outcome simulation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>3D breast scan registration</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Non-rigid surface registration</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Breast imaging</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>415</id>
    <completedYear/>
    <publishedYear>1999</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>367</pageFirst>
    <pageLast>371</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Co-Occurrence Matrizen zur Texturklassifikation in Vektorbildern</title>
    <abstract language="deu">Statistische Eigenschaften natürlicher Grauwerttexturen werden mit Co-Occurrence Matrizen, basierend auf der Grauwertstatistik zweiter Ordnung, modelliert. Die Matrix gibt dann die apriori Wahrscheinlichkeiten aller Grauwertpaare an. Da in der medizinischen Bildverarbeitung verstärkt Multispektralbilder ausgewertet werden, wird das bekannte Konzept hier auf beliebige Vektorbilder erweitert. Dadurch kann bei der Texturklassifikation die zur Verfügung stehende Information vollständig genutzt werden. Insbesondere zur Detektion von Farbtexturen ist dieser Ansatz geeignet, da Wertepaare unterschiedlicher Spektralebenen ausgewertet werden können. Ebenso kann die Methode auch bei der Multiskalendekomposition von Intensitätsbildern zur Verbesserung der Texturerkennung beitragen. Die in den Matrizen entstehenden Muster lassen dann über die Extraktion geeigneter Texturdeskriptoren Rückschlüsse auf die Texturen des Bildes zu.</abstract>
    <parentTitle language="deu">Bildverarbeitung für die Medizin</parentTitle>
    <identifier type="doi">10.1007/978-3-642-60125-5_69</identifier>
    <author>Christoph Palm</author>
    <author>V. Metzler</author>
    <author>B. Moham</author>
    <author>O. Dieker</author>
    <author>Thomas M. Lehmann</author>
    <author>Klaus Spitzer</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Texturerkennung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Vektorbilder</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Multispektralbilder</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Multiskalenbilder</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Klassifikation</value>
    </subject>
    <collection role="ddc" number="005">Computerprogrammierung, Programme, Daten</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>417</id>
    <completedYear/>
    <publishedYear>1998</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>117</pageFirst>
    <pageLast>125</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Distortion Correction of Laryngoscopic Images</title>
    <abstract language="eng">Laryngoscopic images of the vocal tract are used for diagnostic purposes. Quantitative mea-surements like changes of the glottis size or the surface of the vocal cords during an image sequence can be helpful to describe the healing process or to compare the findings of diffe-rent patients. Typically the endoscopic images are circulary symmetric distorted (barrel di-stortion). Therefore measurements of geometric dimensions depend on the object´s position in the image. In this paper an algorithm is presented which allows the computation of the translational invariant "real" object size by correcting the image distortion without using additional calibration of the optical environment.</abstract>
    <parentTitle language="eng">Advances in quantitative laryngoscopy, voice and speech research, Proceedings of the 3rd international workshop Aachen, RWTH</parentTitle>
    <identifier type="url">https://pdfs.semanticscholar.org/e9d8/eb27af24bd79f482821441c2bf0eee7b3fe6.pdf?_ga=2.183754286.985176231.1591560247-1467258391.1581026068</identifier>
    <author>Christoph Palm</author>
    <author>Annegret Pelkmann</author>
    <author>Thomas M. Lehmann</author>
    <author>Klaus Spitzer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>image distortion</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>camera calibration</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multiple regression analysis</value>
    </subject>
    <collection role="ddc" number="005">Computerprogrammierung, Programme, Daten</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>416</id>
    <completedYear/>
    <publishedYear>1999</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>8</pageFirst>
    <pageLast>12</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Wissensbasierte Bewegungskompensation in aktiven Konturmodellen</title>
    <abstract language="deu">Zur Analyse von Lippenbewegungsabläufen wird ein aktives Konturmodell eingesetzt. Probleme bereitet die hohe Sprechgeschwindigkeit, die in star ken Objektverschiebungen result iert und bislang nicht durch eine alleinige Konturanpassung kompensiert werden kann. In diesem Beitrag werden die klassischen aktiven Konturmodelle um eine Vorjustierung der Grobkonturen erweitert, die eine energiebasierte Konturanpassung erst möglich macht. Die Schätzung der Verschiebung zur Vorjustierung basiert auf dem Gradientenbild und einem prädikatenlogisch formulierten Regelwerk, das Annahmen und Nebenbedingungen als Wissensbasis enthält. Mit Hilfe dieser Erweiterungen ist eine automatisierte Konturverfolgung der Lippen möglich.</abstract>
    <parentTitle language="deu">Bildverarbeitung für die Medizin</parentTitle>
    <identifier type="doi">10.1007/978-3-642-60125-5_2</identifier>
    <author>Christoph Palm</author>
    <author>C. Neuschaefer-Rube</author>
    <author>Thomas M. Lehmann</author>
    <author>Klaus Spitzer</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Aktives Konturmodell</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Pradikatenlogik</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Bewegungsschatzung</value>
    </subject>
    <collection role="ddc" number="005">Computerprogrammierung, Programme, Daten</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>420</id>
    <completedYear/>
    <publishedYear>1998</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>412</pageFirst>
    <pageLast>416</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Quantitative Farbmessung in laryngoskopischen Bildern</title>
    <abstract language="deu">Quantitative Farbmessungen sollen die Diagnostik laryngealer Erkrankungen unterstützen. Dabei wird der Farbeindruck nicht nur durch die Reflexionseigenschaften des Gewebes sondern auch durch die Farbe der verwendeten Lichtquelle beeinflußt. Der hier vorgestellte Farbkonstanz-Algorithmus basiert auf dem dichromatischen Reflexionsmodell und liefert eine pixelweise Trennung des Farbbildes in seine beiden Faxbanteile. Die Körperfarbe entspricht dabei der gewebespezifischen Reflexion, die Oberfächenfarbe der Strahlung der Lichtquelle.</abstract>
    <parentTitle language="deu">Bildverarbeitung für die Medizin</parentTitle>
    <identifier type="doi">10.1007/978-3-642-58775-7_81</identifier>
    <author>Christoph Palm</author>
    <author>Ingrid Scholl</author>
    <author>Thomas M. Lehmann</author>
    <author>Klaus Spitzer</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Farbkonstanz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>quantitative Farbmessung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>dichromatisches Reflexionsmodell</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Laryngoskopie</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>5779</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>13</pageNumber>
    <edition/>
    <issue>March</issue>
    <volume>154</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-02-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Error-Correcting Mean-Teacher: Corrections instead of consistency-targets applied to semi-supervised medical image segmentation</title>
    <abstract language="eng">Semantic segmentation is an essential task in medical imaging research. Many powerful deep-learning-based approaches can be employed for this problem, but they are dependent on the availability of an expansive labeled dataset. In this work, we augment such supervised segmentation models to be suitable for learning from unlabeled data. Our semi-supervised approach, termed Error-Correcting Mean-Teacher, uses an exponential moving average model like the original Mean Teacher but introduces our new paradigm of error correction. The original segmentation network is augmented to handle this secondary correction task. Both tasks build upon the core feature extraction layers of the model. For the correction task, features detected in the input image are fused with features detected in the predicted segmentation and further processed with task-specific decoder layers. The combination of image and segmentation features allows the model to correct present mistakes in the given input pair. The correction task is trained jointly on the labeled data. On unlabeled data, the exponential moving average of the original network corrects the student’s prediction. The combined outputs of the students’ prediction with the teachers’ correction form the basis for the semi-supervised update. We evaluate our method with the 2017 and 2018 Robotic Scene Segmentation data, the ISIC 2017 and the BraTS 2020 Challenges, a proprietary Endoscopic Submucosal Dissection dataset, Cityscapes, and Pascal VOC 2012. Additionally, we analyze the impact of the individual components and examine the behavior when the amount of labeled data varies, with experiments performed on two distinct segmentation architectures. Our method shows improvements in terms of the mean Intersection over Union over the supervised baseline and competing methods. Code is available at https://github.com/CloneRob/ECMT.</abstract>
    <parentTitle language="eng">Computers in Biology and Medicine</parentTitle>
    <identifier type="doi">10.1016/j.compbiomed.2023.106585</identifier>
    <identifier type="issn">0010-4825</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-57790</identifier>
    <note>Corresponding author der OTH Regensburg: Robert Mendel</note>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Kostentraeger">2027207</enrichment>
    <enrichment key="CorrespondingAuthor">Robert Mendel</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Robert Mendel</author>
    <author>David Rauber</author>
    <author>Luis Antonio de Souza Jr.</author>
    <author>João Paulo Papa</author>
    <author>Christoph Palm</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Semi-supervised Segmentation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mean-Teacher</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pseudo-labels</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Medical Imaging</value>
    </subject>
    <collection role="ddc" number="00">Informatik, Wissen, Systeme</collection>
    <collection role="ddc" number="61">Medizin und Gesundheit</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="oaweg" number="">Corresponding author der OTH Regensburg</collection>
    <collection role="funding" number="">Publikationsfonds der OTH Regensburg</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/5779/1-s2.0-S0010482523000501-main.pdf</file>
  </doc>
  <doc>
    <id>2166</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2067</pageFirst>
    <pageLast>2068</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>16</volume>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-11-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">IJCARS: BVM 2021 special issue</title>
    <abstract language="eng">The German workshop on medical image computing (BVM) has been held in different locations in Germany for more than 20 years. In terms of content, BVM focused on the computer-aided analysis of medical image data with a wide range of applications, e.g. in the area of imaging, diagnostics, operation planning, computer-aided intervention and visualization.&#13;
&#13;
During this time, there have been remarkable methodological developments and upheavals, on which the BVM community has worked intensively. The area of machine learning should be emphasized, which has led to significant improvements, especially for tasks of classification and segmentation, but increasingly also in image formation and registration. As a result, work in connection with deep learning now dominates the BVM. These developments have also contributed to the establishment of medical image processing at the interface between computer science and medicine as one of the key technologies for the digitization of the health system.&#13;
&#13;
In addition to the presentation of current research results, a central aspect of the BVM is primarily the promotion of young scientists from the diverse BVM community, covering not only Germany but also Austria, Switzerland, The Netherland and other European neighbors. The conference serves primarily doctoral students and postdocs, but also students with excellent bachelor and master theses as a platform to present their work, to enter into professional discourse with the community, and to establish networks with specialist colleagues. Despite the many conferences and congresses that are also relevant for medical image processing, the BVM has therefore lost none of its importance and attractiveness and has retained its permanent place in the annual conference rhythm.&#13;
&#13;
Building on this foundation, there are some innovations and changes this year. The BVM 2021 was organized for the first time at the Ostbayerische Technische Hochschule Regensburg (OTH Regensburg, a technical university of applied sciences). After Aachen, Berlin, Erlangen, Freiburg, Hamburg, Heidelberg, Leipzig, Lübeck, and Munich, Regensburg is not just a new venue. OTH Regensburg is the first representative of the universities of applied sciences (HAW) to organize the conference, which differs to universities, university hospitals, or research centers like Fraunhofer or Helmholtz. This also considers the further development of the research landscape in Germany, where HAWs increasingly contribute to applied research in addition to their focus on teaching. This development is also reflected in the contributions submitted to the BVM in recent years.&#13;
&#13;
At BVM 2021, which was held in a virtual format for the first time due to the Corona pandemic, an attractive and high-quality program was offered. Fortunately, the number of submissions increased significantly. Out of 97 submissions, 26 presentations, 51 posters and 5 software demonstrations were accepted via an anonymized reviewing process with three reviews each. The three best works have been awarded BVM prizes, selected by a separate committee.&#13;
&#13;
Based on these high-quality submissions, we are able to present another special issue in the International Journal of Computer Assisted Radiology and Surgery (IJCARS). Out of the 97 submissions, the ones with the highest scores have been invited to submit an extended version of their paper to be presented in IJCARS. As a result, we are now able to present this special issue with seven excellent articles. Many submissions focus on machine learning in a medical context.</abstract>
    <parentTitle language="eng">International Journal of Computer Assisted Radiology and Surgery</parentTitle>
    <identifier type="doi">10.1007/s11548-021-02534-7</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-21666</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Andreas Maier</author>
    <author>Thomas M. Deserno</author>
    <author>Heinz Handels</author>
    <author>Klaus H. Maier-Hein</author>
    <author>Christoph Palm</author>
    <author>Thomas Tolxdorff</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Medical Image Computing</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Medizin</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/2166/Maier2021_Article_IJCARSBVM2021SpecialIssue.pdf</file>
  </doc>
  <doc>
    <id>7866</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>312</pageFirst>
    <pageLast>321</pageLast>
    <pageNumber/>
    <edition/>
    <issue>5</issue>
    <volume>28</volume>
    <type>article</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-12-30</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">KI-gestützte Untersuchung in der nicht-operativen Versorgung symptomgebender Erkrankungen des Kniegelenks – ein multiprofessionelles Konzept (KINEESIO)</title>
    <abstract language="deu">Beschwerdebilder am Kniegelenk aufgrund muskuloskelettaler degenerativer oder verletzungsbedingter Erkrankungen sind häufig, nehmen im Alter zu und sind mit der steigenden Inanspruchnahme ärztlicher und therapeutischer Behandlungsmaßnahmen verbunden. Einer erfolgreichen Therapie gehen oft notwendige zeit- und ressourcenaufwendige Untersuchungen zur Erkennung und Differenzierung der patient*innenspezifischen Problematik voraus. Im Zusammenhang mit der nicht-operativen Versorgung des Kniegelenks hat ein sektorübergreifendes multiprofessionelles Forschungsteam ein Konzept entwickelt, um künstliche neuronale Netze so zu trainieren, dass sie bei der ärztlichen und physiotherapeutischen Untersuchung unterstützend Einsatz finden können. Denn gerade in der Erfassung und Auswertung umfassender Datenmengen liegen große Potenziale in der Künstlichen Intelligenz (KI) im Gesundheitswesen. Das Projekt KINEESIO trainiert und testet KI-gestützte Screening- Tools zur Untersuchung von Patient*innen mit Kniegelenkerkrankungen. Diese unterstützen die Abläufe zwischen Leistungserbringern und Patient*innen, tragen zu einer verbesserten Differenzierung individueller Beschwerdebilder bei und dienen Entscheidungsprozessen für eine adäquate Versorgung. Dadurch sollen Ressourcen im Gesundheitswesen geschont und eine qualitativ hochwertige Therapie ausreichend ermöglicht werden.</abstract>
    <abstract language="eng">Complaints regarding knee joints resulting from musculoskeletal degenerative or injury-related conditions are common, increase with age, and are associated with rising demand for treatments by physicians and therapists. The success of a therapeutic intervention is frequently contingent upon necessary time- and resource-intensive examinations to identify and differentiate the patient’s specific issues. In connection with non-surgical knee joint treatment, a crosssectoral, multidisciplinary research team has developed a concept for training artificial neural networks to assist in medical and physiotherapeutic examinations. After all, there is great potential for artificial intelligence (AI) in healthcare, particularly in collecting and analyzing large amounts of data. The KINEESIO project is training and testing screening tools supported by artificial intelligence for the assessment of patients with knee joint disorders. These tools support the workflows between healthcare providers and patients, improve the differentiation of individual symptoms and serve the decisionmaking process towards appropriate care. This should conserve healthcare resources and enable sufficient high-quality treatment.</abstract>
    <parentTitle language="deu">MSK – Muskuloskelettale Physiotherapie</parentTitle>
    <identifier type="doi">10.1055/a-2402-9982</identifier>
    <identifier type="issn">2701-6986</identifier>
    <enrichment key="BegutachtungStatus">begutachtet</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Elke Schulze</author>
    <author>Christoph Palm</author>
    <author>Maximilian Kerschbaum</author>
    <author>Roman Seidel</author>
    <author>Lars Lehmann</author>
    <author>Michael Koller</author>
    <author>Andrea Pfingsten</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Physiotherapie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Diagnose</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Kniegelenk</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="FakSoz">Fakultät Sozial- und Gesundheitswissenschaften</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="pfingstenlphpub">Pfingsten, Andrea (Prof. Dr.), Publikationen  - Labor Physiotherapie</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Physiotherapie (LPh)</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>425</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1980</pageFirst>
    <pageLast>1987</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>44</volume>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Heidelberg</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-05-29</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Novel Method of Outcome Assessment in Breast Reconstruction Surgery: Comparison of Autologous and Alloplastic Techniques Using Three-Dimensional Surface Imaging</title>
    <abstract language="eng">Background &#13;
Breast reconstruction is an important coping tool for patients undergoing a mastectomy. There are numerous surgical techniques in breast reconstruction surgery (BRS). Regardless of the technique used, creating a symmetric outcome is crucial for patients and plastic surgeons. Three-dimensional surface imaging enables surgeons and patients to assess the outcome’s symmetry in BRS. To discriminate between autologous and alloplastic techniques, we analyzed both techniques using objective optical computerized symmetry analysis. Software was developed that enables clinicians to assess optical breast symmetry using three-dimensional surface imaging. &#13;
Methods&#13;
Twenty-seven patients who had undergone autologous (n = 12) or alloplastic (n = 15) BRS received three-dimensional surface imaging. Anthropomorphic data were collected digitally using semiautomatic measurements and automatic measurements. Automatic measurements were taken using the newly developed software. To quantify symmetry, a Symmetry Index is proposed.&#13;
Results &#13;
Statistical analysis revealed that there is no dif- ference in the outcome symmetry between the two groups (t test for independent samples; p = 0.48, two-tailed). &#13;
Conclusion&#13;
This study’s findings provide a foundation for qualitative symmetry assessment in BRS using automatized digital anthropometry. In the present trial, no difference in the outcomes’ optical symmetry was detected between autologous and alloplastic approaches.</abstract>
    <parentTitle language="eng">Aesthetic Plastic Surgery</parentTitle>
    <identifier type="doi">10.1007/s00266-020-01749-4</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Robin Hartmann</author>
    <author>Maximilian Weiherer</author>
    <author>Daniel Schiltz</author>
    <author>Stephan Seitz</author>
    <author>Luisa Lotter</author>
    <author>Alexandra Anker</author>
    <author>Christoph Palm</author>
    <author>Lukas Prantl</author>
    <author>Vanessa Brébant</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Breast reconstruction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Breast symmetry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital anthropometry</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Mammoplastik</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Dreidimensionale Bildverarbeitung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Autogene Transplantation</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Alloplastik</value>
    </subject>
    <collection role="ddc" number="617">Chirurgie und verwandte medizinische Fachrichtungen</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>673</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>721</pageFirst>
    <pageLast>728</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>303</volume>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Heidelberg</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-11-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">New aspects in digital breast assessment: further refinement of a method for automated digital anthropometry</title>
    <abstract language="eng">Purpose: In this trial, we used a previously developed prototype software to assess aesthetic results after reconstructive surgery for congenital breast asymmetry using automated anthropometry. To prove the consensus between the manual and automatic digital measurements, we evaluated the software by comparing the manual and automatic measurements of 46 breasts.&#13;
&#13;
Methods: Twenty-three patients who underwent reconstructive surgery for congenital breast asymmetry at our institution were examined and underwent 3D surface imaging. Per patient, 14 manual and 14 computer-based anthropometric measurements were obtained according to a standardized protocol. Manual and automatic measurements, as well as the previously proposed Symmetry Index (SI), were compared.&#13;
&#13;
Results: The Wilcoxon signed-rank test revealed no significant differences in six of the seven measurements between the automatic and manual assessments. The SI showed robust agreement between the automatic and manual methods.&#13;
&#13;
Conclusion: The present trial validates our method for digital anthropometry. Despite the discrepancy in one measurement, all remaining measurements, including the SI, showed high agreement between the manual and automatic methods. The proposed data bring us one step closer to the long-term goal of establishing robust instruments to evaluate the results of breast surgery.</abstract>
    <parentTitle language="eng">Archives of Gynecology and Obstetrics</parentTitle>
    <identifier type="doi">10.1007/s00404-020-05862-2</identifier>
    <identifier type="issn">1432-0711</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Robin Hartmann</author>
    <author>Maximilian Weiherer</author>
    <author>Daniel Schiltz</author>
    <author>Magnus Baringer</author>
    <author>Vivien Noisser</author>
    <author>Vanessa Hösl</author>
    <author>Andreas Eigenberger</author>
    <author>Stefan Seitz</author>
    <author>Christoph Palm</author>
    <author>Lukas Prantl</author>
    <author>Vanessa Brébant</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>digital anthropometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>reconstructive surgery</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>3D surface imaging</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>660</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>141</pageFirst>
    <pageLast>157</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-10-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Semi-supervised Segmentation Based on Error-Correcting Supervision</title>
    <abstract language="eng">Pixel-level classification is an essential part of computer vision. For learning from labeled data, many powerful deep learning models have been developed recently. In this work, we augment such supervised segmentation models by allowing them to learn from unlabeled data. Our semi-supervised approach, termed Error-Correcting Supervision, leverages a collaborative strategy. Apart from the supervised training on the labeled data, the segmentation network is judged by an additional network. The secondary correction network learns on the labeled data to optimally spot correct predictions, as well as to amend incorrect ones. As auxiliary regularization term, the corrector directly influences the supervised training of the segmentation network. On unlabeled data, the output of the correction network is essential to create a proxy for the unknown truth. The corrector’s output is combined with the segmentation network’s prediction to form the new target. We propose a loss function that incorporates both the pseudo-labels as well as the predictive certainty of the correction network. Our approach can easily be added to supervised segmentation models. We show consistent improvements over a supervised baseline on experiments on both the Pascal VOC 2012 and the Cityscapes datasets with varying amounts of labeled data.</abstract>
    <parentTitle language="eng">Computer vision - ECCV 2020: 16th European conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXIX</parentTitle>
    <identifier type="isbn">978-3-030-58525-9</identifier>
    <identifier type="doi">10.1007/978-3-030-58526-6_9</identifier>
    <enrichment key="OtherSeries">Lecture Notes in Computer Science; 12374</enrichment>
    <enrichment key="ConferenceStatement">European Conference on Computer Vision, 16th, 2020</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Robert Mendel</author>
    <author>Luis Antonio de Souza Jr.</author>
    <author>David Rauber</author>
    <author>João Paulo Papa</author>
    <author>Christoph Palm</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Semi-Supervised Learning</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Machine Learning</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>423</id>
    <completedYear/>
    <publishedYear>1996</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>229</pageFirst>
    <pageLast>234</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Verlag der Augustinus-Buchhandlung</publisherName>
    <publisherPlace>Aachen</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Trennung von diffuser und spiegelnder Reflexion in Farbbildern des Larynx zur Untersuchung von Farb- und Formmerkmalen der Stimmlippen</title>
    <abstract language="deu">Zur diagnostischen Unterstützung bei der Befundung laryngealer Erkrankungen soll eine Farb- und Formanalyse der Stimmlippen durchgeführt werden. In diesem Beitrag wird ein Verfahren zur Trennung der spiegelnden und diffusen Reflexionsanteile in Farbbildern des Larynx vorgestellt. Die Farbe der diffusen Komponente entspricht dabei der beleuchtungsunabhängigen Objektfarbe, während deren Wichtungsfaktoren als Eingabe für Shape-from-Shading-Verfahren zur Oberflächenrekonstruktion dienen.</abstract>
    <parentTitle language="deu">Bildverarbeitung für die Medizin. Algorithmen, Systeme, Anwendungen. Proceedings des Aachener Workshops am 8. und 9. November 1996</parentTitle>
    <identifier type="url">https://scholar.google.de/citations?user=nc0XkcMAAAAJ&amp;hl=fa#d=gs_md_cita-d&amp;u=%2Fcitations%3Fview_op%3Dview_citation%26hl%3Dfa%26user%3Dnc0XkcMAAAAJ%26citation_for_view%3Dnc0XkcMAAAAJ%3AqjMakFHDy7sC%26tzom%3D-120</identifier>
    <author>Christoph Palm</author>
    <author>Ingrid Scholl</author>
    <author>Thomas M. Lehmann</author>
    <author>Klaus Spitzer</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Laryngoskopie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Farbbild</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Reflexion</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>422</id>
    <completedYear/>
    <publishedYear>1998</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>135</pageFirst>
    <pageLast>142</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Objective Judgement of Endoscopic Laryngeal Images</title>
    <abstract language="deu">Video Documentation of endoscopic findings simplifies diagnostic  counseling of the patient and aids pre-operative discussion among the medical team. Judgment of such images is still subjective and can  not  give a quantitative evaluation of the disease process  regarding diagnosis or response to treatment. Modern treatment of early  laryngeal cancer with laserablation requires intensive follow up and  frequent direct laryngoscopy under general anesthesia with blind  biopsies to detect any tumor residual or recurrence.  Inflammatory conditions of the larynx are frequently confused with other causes of dysphonia. Mapping anddigital analysis  of the documented image will suggest the tumor site and avoids undue blind biopsies under  anesthesia. However, varying illumination results in different colors  reflected from the same object. To achieve quantitative analysis, color constancy has to be assured. Inthis paper, the environment is presented which allow the objective judgment of larngoscopies.</abstract>
    <parentTitle language="eng">Advances in Quantitative Laryngoscopy, Voice and Speech Research, Proceedings of the 3rd International Workshop, RWTH Aachen</parentTitle>
    <identifier type="url">https://citeseerx.ist.psu.edu/doc_view/pid/caf5bedf5cf68ed3be68054b140a1241f4f278e2</identifier>
    <author>H. Hassan</author>
    <author>Justus F. R. Ilgner</author>
    <author>Christoph Palm</author>
    <author>Thomas M. Lehmann</author>
    <author>Klaus Spitzer</author>
    <author>Martin Westhofen</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laryngoscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Diagnosis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image  processing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Quantitative  Image  analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Colorconstancy</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>421</id>
    <completedYear/>
    <publishedYear>1997</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>81</pageFirst>
    <pageLast>86</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Quantitative Analyse der Stimmlippen</title>
    <parentTitle language="deu">5. Workshop Digitale Bildverarbeitung in der Medizin, Universität Freiburg, 10.-11. März 1997</parentTitle>
    <identifier type="url">https://pdfs.semanticscholar.org/9a0c/9e7dc883ccf6e8a8a686c28422238adb5f35.pdf</identifier>
    <author>Ingrid Scholl</author>
    <author>Christoph Palm</author>
    <author>Abhijit Sovakar</author>
    <author>Thomas M. Lehmann</author>
    <author>Klaus Spitzer</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Konturverfolgung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Snakes</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Farbanalyse</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Dichromatisches Reflexionsmodell</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Farbkonstanz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Laryngoskopie</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>3512</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferencepresentation</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-04-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Blutdruck, Hämodynamik und Gefäßzustand: Innovative Erfassung und Bewertung – Schwerpunkt bildbasierte Verfahren</title>
    <abstract language="deu">Einleitung: &#13;
Blutdruck gilt als sogenannter Vitalparameter als einer der grundlegenden Indikatoren für den Gesundheitszustand einer Person. Sowohl zu niedriger als auch zu hoher Blutdruck kann lebensbedrohend sein, letzerer ist darüber hinaus ein Risikofaktor insbesondere für Herz-Kreislauferkrankungen, die trotz wichtiger Fortschritte in der Behandlung immer noch die häufigste Todesursache in Deutschland darstellen. Die Hämodynamik, also die raumzeitliche Dynamik des Blutflusses, und der Gefäßzustand sind eng verbunden mit dem Blutdruck und ebenfalls von hoher klinischer Relevanz, u.a. zur Identifikation von Durchblutungsstörungen und ungünstigen Druckverteilungen der Gefäßwand. Innovationen in der Messtechnik als auch in der Datenanalyse bieten heute neue Möglichkeiten der Erfassung und Bewertung von Blutdruck, Hämodynamik und Gefäßzustand [1], [2], [3], [4].&#13;
&#13;
Methodik:&#13;
In einer gemeinsamen Workshopserie der AG Medizinische Bild- und Signalverarbeitung der GMDS und des Fachausschusses Biosignale der DGBMT werden wir neue Ansätze und Lösungen für Mess- und Analyseverfahren zu Blutdruck und -fluss sowie zum Gefäßzustand vorstellen und diskutieren. Dabei stehen im ersten Workshop auf der GMDS Jahrestagung Bildbasierte Verfahren im Zentrum, während der zweite Workshop auf der DGBMT Jahrestagung den Fokus auf Biosignalbasierten Verfahren legt. Es werden aktuelle Forschungsergebnisse vorgestellt und diskutiert. Es sind jeweils mehrere Vorträge geplant mit ausreichend Zeit zur Diskussion. Folgende Vorträge sind geplant (Arbeitstitel):&#13;
&#13;
Sebastian Zaunseder: Videobasierte Erfassung des Blutdrucks&#13;
Anja Hennemuth: A Visualization Toolkit for the Analysis of Aortic Anatomy and Pressure Distribution&#13;
Lars Krenkel: Numerische Analyse der Rupturwahrscheinlichkeit zerebraler Aneurysmata&#13;
Susanne Schnell: Messung des Blutflusses und hämodynamischer Parameter mit 4D flow MRI: Möglichkeiten und Herausforderungen&#13;
&#13;
Ergebnisse: &#13;
Ziel des Workshops ist die Identifikation von innovativen Ansätzen und neuen Methoden zur qualitativen und quantitativen Bestimmung von hämodynamischen Parametern sowie deren kritische Bewertung durch die Community für die Eignung in der klinischen Entscheidungsunterstützung.&#13;
&#13;
Diskussion:&#13;
Der Workshop leistet inhaltlich einen Beitrag zu zentralen Aspekten für die Herz-Kreislauf-Medizin. Er bringt dabei Expertise aus verschiedenen Bereichen zusammen und schlägt die Brücke zwischen Kardiologie, Medizininformatik und Medizintechnik.&#13;
&#13;
Schlussfolgerung:&#13;
Innovative Technologien aus Medizintechnik und Informatik ermöglichen zunehmend einfache und raumzeitlich aufgelöste Erfassung und Bewertung wichtiger Informationen zur Unterstützung von Diagnose und Therapieverfolgung.&#13;
&#13;
[1] Zaunseder S, Trumpp A, Wedekind D, Malberg H. Cardiovascular assessment by imaging photoplethysmography - a review. Biomed Tech (Berl). 2018 Oct 25;63(5):617–34.&#13;
[2] Huellebrand M, Messroghli D, Tautz L, Kuehne T, Hennemuth A. An extensible software platform for interdisciplinary cardiovascular imaging research. Comput Methods Programs Biomed. 2020 Feb;184:105277.&#13;
[3] Schmitter S, Adriany G, Waks M, Moeller S, Aristova M, Vali A, et al. Bilateral Multiband 4D Flow MRI of the Carotid Arteries at 7T. Magn Reson Med. 2020 Oct;84(4):1947–60.&#13;
[4] Birkenmaier C, and Krenkel, L.  Flow in Artificial Lungs. In: New Results in Numerical and Experimental Fluid Mechanics XIII. Contributions to the 22nd STAB/DGLR Symposium. Springer; 2021.</abstract>
    <parentTitle language="deu">66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e. V. (TMF), 26. - 30.09.2021, online</parentTitle>
    <identifier type="doi">10.3205/21gmds016</identifier>
    <identifier type="urn">urn:nbn:de:0183-21gmds0167</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Dagmar Krefting</author>
    <author>Sebastian Zaunseder</author>
    <author>Dennis Säring</author>
    <author>Thomas Wittenberg</author>
    <author>Christoph Palm</author>
    <author>Karin Schiecke</author>
    <author>Lars Krenkel</author>
    <author>Anja Hennemuth</author>
    <author>Susanne Schnell</author>
    <author>Nicolai Spicher</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Bildbasierte Verfahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Blutdruck</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Hämodynamik</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Blutgefäß</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildgebendes Verfahren</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="krenkellbfmconf">Krenkel, Lars (Prof. Dr.), Präsentationen - Labor Biofluidmechanik</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <collection role="institutes" number="">Labor Biomechanik (LBM)</collection>
  </doc>
  <doc>
    <id>101</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1410</pageFirst>
    <pageLast>1420</pageLast>
    <pageNumber/>
    <edition/>
    <issue>9</issue>
    <volume>125</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-12-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography</title>
    <abstract language="eng">Purpose&#13;
Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these require in-depth and time-consuming analysis of fundus images. Herein, we present an automated computer-based classification algorithm.&#13;
Design Algorithm development for AMD classification based on a large collection of color fundus images. Validation is performed on a cross-sectional, population-based study.&#13;
Participants.&#13;
&#13;
We included 120 656 manually graded color fundus images from 3654 Age-Related Eye Disease Study (AREDS) participants. AREDS participants were &gt;55 years of age, and non-AMD sight-threatening diseases were excluded at recruitment. In addition, performance of our algorithm was evaluated in 5555 fundus images from the population-based Kooperative Gesundheitsforschung in der Region Augsburg (KORA; Cooperative Health Research in the Region of Augsburg) study.&#13;
Methods.&#13;
&#13;
We defined 13 classes (9 AREDS steps, 3 late AMD stages, and 1 for ungradable images) and trained several convolution deep learning architectures. An ensemble of network architectures improved prediction accuracy. An independent dataset was used to evaluate the performance of our algorithm in a population-based study.&#13;
Main Outcome Measures.&#13;
&#13;
κ Statistics and accuracy to evaluate the concordance between predicted and expert human grader classification.&#13;
Results.&#13;
&#13;
A network ensemble of 6 different neural net architectures predicted the 13 classes in the AREDS test set with a quadratic weighted κ of 92% (95% confidence interval, 89%–92%) and an overall accuracy of 63.3%. In the independent KORA dataset, images wrongly classified as AMD were mainly the result of a macular reflex observed in young individuals. By restricting the KORA analysis to individuals &gt;55 years of age and prior exclusion of other retinopathies, the weighted and unweighted κ increased to 50% and 63%, respectively. Importantly, the algorithm detected 84.2% of all fundus images with definite signs of early or late AMD. Overall, 94.3% of healthy fundus images were classified correctly.&#13;
&#13;
Conclusions&#13;
Our deep learning algoritm revealed a weighted κ outperforming human graders in the AREDS study and is suitable to classify AMD fundus images in other datasets using individuals &gt;55 years of age.</abstract>
    <parentTitle language="eng">Ophtalmology</parentTitle>
    <identifier type="doi">10.1016/j.ophtha.2018.02.037</identifier>
    <note>Corresponding authors: Bernhard H. F. Weber, University of Regensburg, and Christoph Palm</note>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Felix Graßmann</author>
    <author>Judith Mengelkamp</author>
    <author>Caroline Brandl</author>
    <author>Sebastian Harsch</author>
    <author>Martina E. Zimmermann</author>
    <author>Birgit Linkohr</author>
    <author>Annette Peters</author>
    <author>Iris M. Heid</author>
    <author>Christoph Palm</author>
    <author>Bernhard H. F. Weber</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Senile Makuladegeneration</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Krankheitsverlauf</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Mustererkennung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="oaweg" number="">Corresponding author der OTH Regensburg</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>7928</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>17</pageNumber>
    <edition/>
    <issue/>
    <volume>29</volume>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Evaluating smartphone-based 3D imaging techniques for clinical application in oral and maxillofacial surgery: A comparative study with the vectra M5</title>
    <abstract language="eng">PURPOSE&#13;
This study aimed to clarify the applicability of smartphone-based three-dimensional (3D) surface imaging for clinical use in oral and maxillofacial surgery, comparing two smartphone-based approaches to the gold standard.&#13;
METHODS&#13;
Facial surface models (SMs) were generated for 30 volunteers (15 men, 15 women) using the Vectra M5 (Canfield Scientific, USA), the TrueDepth camera of the iPhone 14 Pro (Apple Inc., USA), and the iPhone 14 Pro with photogrammetry. Smartphone-based SMs were superimposed onto Vectra-based SMs. Linear measurements and volumetric evaluations were performed to evaluate surface-to-surface deviation. To assess inter-observer reliability, all measurements were performed independently by a second observer. Statistical analyses included Bland-Altman analyses, the Wilcoxon signed-rank test for paired samples, and Intraclass correlation coefficients.&#13;
RESULTS&#13;
Photogrammetry-based SMs exhibited an overall landmark-to-landmark deviation of M = 0.8 mm (SD =  ± 0.58 mm, n = 450), while TrueDepth-based SMs displayed a deviation of M = 1.1 mm (SD =  ± 0.72 mm, n = 450). The mean volumetric difference for photogrammetry-based SMs was M = 1.8 cc (SD =  ± 2.12 cc, n = 90), and M = 3.1 cc (SD =  ± 2.64 cc, n = 90) for TrueDepth-based SMs. When comparing the two approaches, most landmark-to-landmark measurements demonstrated 95% Bland-Altman limits of agreement (LoA) of ≤ 2 mm. Volumetric measurements revealed LoA &gt; 2 cc. Photogrammetry-based measurements demonstrated higher inter-observer reliability for overall landmark-to-landmark deviation.&#13;
CONCLUSION&#13;
Both approaches for smartphone-based 3D surface imaging exhibit potential in capturing the face. Photogrammetry-based SMs demonstrated superior alignment and volumetric accuracy with Vectra-based SMs than TrueDepth-based SMs.</abstract>
    <parentTitle language="eng">Oral and Maxillofacial Surgery</parentTitle>
    <identifier type="doi">10.1007/s10006-024-01322-2</identifier>
    <identifier type="pmid">39792225</identifier>
    <enrichment key="opus.import.date">2025-01-20T09:16:32+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
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    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Robin Hartmann</author>
    <author>Maximilian Weiherer</author>
    <author>Felix Nieberle</author>
    <author>Christoph Palm</author>
    <author>Vanessa Brébant</author>
    <author>Lukas Prantl</author>
    <author>Philipp Lamby</author>
    <author>Torsten E. Reichert</author>
    <author>Jürgen Taxis</author>
    <author>Tobias Ettl</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Three-dimensional Surface Imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Smartphone-based Surface Imaging     TrueDepth     Stereophotogrammetry     Oral and Maxillofacial Surgery</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>TrueDepth</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Stereophotogrammetry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Oral and Maxillofacial Surgery</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>96</id>
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    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>10</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>114</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-12-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Force-feedback assisted and virtual fixtures based K-wire drilling simulation</title>
    <abstract language="eng">One common method to fix fractures of the human hand after an accident is an osteosynthesis with Kirschner wires (K-wires) to stabilize the bone fragments. The insertion of K-wires is a delicate minimally invasive surgery, because surgeons operate almost without a sight. Since realistic training methods are time consuming, costly and insufficient, a virtual-reality (VR) based training system for the placement of K-wires was developed. As part of this, the current work deals with the real-time bone drilling simulation using a haptic force-feedback device.&#13;
&#13;
To simulate the drilling, we introduce a virtual fixture based force-feedback drilling approach. By decomposition of the drilling task into individual phases, each phase can be handled individually to perfectly control the drilling procedure. We report about the related finite state machine (FSM), describe the haptic feedback of each state and explain, how to avoid jerking of the haptic force-feedback during state transition.&#13;
&#13;
The usage of the virtual fixture approach results in a good haptic performance and a stable drilling behavior. This was confirmed by 26 expert surgeons, who evaluated the virtual drilling on the simulator and rated it as very realistic. To make the system even more convincing, we determined real drilling feed rates through experimental pig bone drilling and transferred them to our system. Due to a constant simulation thread we can guarantee a precise drilling motion.&#13;
&#13;
Virtual fixtures based force-feedback calculation is able to simulate force-feedback assisted bone drilling with high quality and, thus, will have a great potential in developing medical applications.</abstract>
    <parentTitle language="eng">Computers in Biology and Medicine</parentTitle>
    <identifier type="doi">10.1016/j.compbiomed.2019.103473</identifier>
    <note>Corresponding author: Christoph Palm</note>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Johannes Maier</author>
    <author>Jerome Perret</author>
    <author>Martina Simon</author>
    <author>Stephanie Schmitt-Rüth</author>
    <author>Thomas Wittenberg</author>
    <author>Christoph Palm</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Handchirurgie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Osteosynthese</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Operationstechnik</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Lernprogramm</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Virtuelle Realität</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Medical training system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Virtual fixtures</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Virtual reality</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Force-feedback haptic</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Minimally invasive hand surgery</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>K-wire drilling</value>
    </subject>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
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    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmhaptivist">Palm, Christoph (Prof. Dr.) - Projekt HaptiVisT</collection>
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    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="oaweg" number="">Corresponding author der OTH Regensburg</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>7800</id>
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    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
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    <publisherName>IEEE</publisherName>
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    <title language="eng">LiwTERM: A Lightweight Transformer-Based Model for Dermatological Multimodal Lesion Detection</title>
    <abstract language="eng">Skin cancer is the most common type of cancer in the world, accounting for approximately 30% of all diagnosed tumors. Early diagnosis reduces mortality rates and prevents disfiguring effects in different body regions. In recent years, machine learning techniques, particularly deep learning, have shown promising results in this task, presenting studies that have demonstrated that combining a patient’s clinical information with images of the lesion is crucial for improving the classification of skin lesions. Despite that, meaningful use of clinical information with multiple images is mandatory, requiring further investigation. Thus, this project aims to contribute to developing multimodal machine learning-based models to cope with the skin lesion classification task employing a lightweight transformer model. As a main hypothesis, models can take multiple images from different sources as input, along with clinical information from the patient’s history, leading to a more reliable diagnosis. Our model deals with the not-trivial task of combining images and clinical information (from anamneses) concerning the skin lesions in a lightweight transformer architecture that does not demand high computation resources but still presents competitive classification results.</abstract>
    <parentTitle language="eng">2024 37th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Manaus, Brazil, 9/30/2024 - 10/3/2024</parentTitle>
    <identifier type="isbn">979-8-3503-7603-6</identifier>
    <identifier type="doi">10.1109/SIBGRAPI62404.2024.10716324</identifier>
    <enrichment key="opus.import.date">2024-11-18T10:16:05+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Luis A. Souza</author>
    <author>André G.C. Pacheco</author>
    <author>Gabriel G. de Angelo</author>
    <author>Thiago Oliveira-Santos</author>
    <author>Christoph Palm</author>
    <author>João Paulo Papa</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Lightweight Architectures</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Trans- formers</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Skin Lesion Detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>1459</id>
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    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>205</pageFirst>
    <pageLast>210</pageLast>
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    <publisherName>Springer Vieweg</publisherName>
    <publisherPlace>Wiesbaden</publisherPlace>
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    <title language="eng">Fine-tuning Generative Adversarial Networks using Metaheuristics</title>
    <abstract language="eng">Barrett's esophagus denotes a disorder in the digestive system that affects the esophagus' mucosal cells, causing reflux, and showing potential convergence to esophageal adenocarcinoma if not treated in initial stages. Thus, fast and reliable computer-aided diagnosis becomes considerably welcome. Nevertheless, such approaches usually suffer from imbalanced datasets, which can be addressed through Generative Adversarial Networks (GANs). Such techniques generate realistic images based on observed samples, even though at the cost of a proper selection of its hyperparameters. Many works employed a class of nature-inspired algorithms called metaheuristics to tackle the problem considering distinct deep learning approaches. Therefore, this paper's main contribution is to introduce metaheuristic techniques to fine-tune GANs in the context of Barrett's esophagus identification, as well as to investigate the feasibility of generating high-quality synthetic images for early-cancer assisted identification.</abstract>
    <parentTitle language="eng">Bildverarbeitung für die Medizin 2021. Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021</parentTitle>
    <subTitle language="eng">A Case Study on Barrett's Esophagus Identification</subTitle>
    <identifier type="isbn">978-3-658-33197-9</identifier>
    <identifier type="doi">10.1007/978-3-658-33198-6_50</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Luis Antonio de Souza Jr.</author>
    <author>Leandro A. Passos</author>
    <author>Robert Mendel</author>
    <author>Alanna Ebigbo</author>
    <author>Andreas Probst</author>
    <author>Helmut Messmann</author>
    <author>Christoph Palm</author>
    <author>João Paulo Papa</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Endoskopie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Computerunterstützte Medizin</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Deep Learning</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="ddc" number="6">Technik, Medizin, angewandte Wissenschaften</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="palmbarrett">Palm, Christoph (Prof. Dr.) - Projekt Barrett</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>115</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>395</pageFirst>
    <pageLast>400</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">GraphMIC: Medizinische Bildverarbeitung in der Lehre</title>
    <abstract language="deu">Die Lehre der medizinischen Bildverarbeitung vermittelt Kenntnisse mit einem breiten Methodenspektrum. Neben den Grundlagen der Verfahren soll ein Gefühl für eine geeignete Ausführungsreihenfolge und ihrer Wirkung auf medizinische Bilddaten entwickelt werden. Die Komplexität der Methoden erfordert vertiefte Programmierkenntnisse, sodass bereits einfache Operationen mit großem Programmieraufwand verbunden sind. Die Software GraphMIC stellt Bildverarbeitungsoperationen in Form interaktiver Knoten zur Verfügung und erlaubt das Arrangieren, Parametrisieren und Ausführen komplexer Verarbeitungssequenzen in einem Graphen. Durch den Fokus auf das Design einer Pipeline, weg von sprach- und frameworkspezifischen Implementierungsdetails, lassen sich grundlegende Prinzipien der Bildverarbeitung anschaulich erlernen. In diesem Beitrag stellen wir die visuelle Programmierung mit GraphMIC der nativen Implementierung äquivalenter Funktionen gegenüber. Die in C++ entwickelte Applikation basiert auf Qt, ITK, OpenCV, VTK und MITK.</abstract>
    <parentTitle language="deu">Bildverarbeitung für die Medizin 2015; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 15. bis 17. März 2015 in Lübeck</parentTitle>
    <identifier type="doi">10.1007/978-3-662-46224-9_68</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Alexander Eduard Szalo</author>
    <author>Alexander Zehner</author>
    <author>Christoph Palm</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bildverarbeitung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Medizin</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Hochschuldidaktik</value>
    </subject>
    <collection role="ddc" number="0">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>5916</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>49</pageFirst>
    <pageLast>55</pageLast>
    <pageNumber/>
    <edition>1. Aufl.</edition>
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    <type>bookpart</type>
    <publisherName>Wiley</publisherName>
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    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-03-21</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">History, Core Concepts, and Role of AI in Clinical Medicine</title>
    <abstract language="eng">The field of AI is characterized by robust promises, astonishing successes, and remarkable breakthroughs. AI will play a major role in all domains of clinical medicine, but the role of AI in relation to the physician is not yet completely determined. The term artificial intelligence or AI is broad, and several different terms are used in this context that must be organized and demystified. This chapter will review the key concepts and methods of AI, and will introduce some of the different roles for AI in relation to the physician.</abstract>
    <parentTitle language="eng">AI in Clinical Medicine: A Practical Guide for Healthcare Professionals</parentTitle>
    <identifier type="isbn">978-1-119-79064-8</identifier>
    <identifier type="doi">10.1002/9781119790686.ch5</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Christoph Palm</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>artificial intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>healthcare</value>
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    <collection role="ddc" number="00">Informatik, Wissen, Systeme</collection>
    <collection role="ddc" number="61">Medizin und Gesundheit</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
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