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  <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>
    <completedYear/>
    <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/>
    <creatingCorporation/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-12-17</completedDate>
    <publishedDate>--</publishedDate>
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    <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>
    <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="palmhaptivist">Palm, Christoph (Prof. Dr.) - Projekt HaptiVisT</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="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
  <doc>
    <id>7800</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
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    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
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    <contributingCorporation/>
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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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    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
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    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <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>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>205</pageFirst>
    <pageLast>210</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer Vieweg</publisherName>
    <publisherPlace>Wiesbaden</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
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      <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>
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