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    <title language="eng">From Image Data to Three-Dimensional Models - Case Studies on the Impact of 3D Patient Models</title>
    <parentTitle language="eng">Proceedings of the Japan Korea Computer Graphics Conference 2002</parentTitle>
    <author>Hans-Christian Hege</author>
    <author>Hartmut Schirmacher</author>
    <author>Malte Westerhoff</author>
    <author>Hans Lamecker</author>
    <author>Steffen Prohaska</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="visalgo">Visual Data Analysis in Science and Engineering</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="persons" number="lamecker">Lamecker, Hans</collection>
    <collection role="persons" number="prohaska">Prohaska, Steffen</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="MATHEON-F2">MATHEON-F2</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>3824</id>
    <completedYear>2006</completedYear>
    <publishedYear>2006</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>393</pageFirst>
    <pageLast>395</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>1(1)</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automatic segmentation of mandibles in low-dose CT-data</title>
    <parentTitle language="eng">Int. J. Computer Assisted Radiology and Surgery</parentTitle>
    <author>Hans Lamecker</author>
    <author>Stefan Zachow</author>
    <author>Antonia Wittmers</author>
    <author>Britta Weber</author>
    <author>Hans-Christian Hege</author>
    <author>Barbara Elsholtz</author>
    <author>Michael Stiller</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="persons" number="lamecker">Lamecker, Hans</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="Dental">Dental</collection>
    <collection role="projects" number="MATHEON-F2">MATHEON-F2</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>3825</id>
    <completedYear>2006</completedYear>
    <publishedYear>2006</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>253</pageFirst>
    <pageLast>254</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>1(1)</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Surgical treatment of craniosynostosis based on a statistical 3D-shape model</title>
    <parentTitle language="eng">Int. J. Computer Assisted Radiology and Surgery</parentTitle>
    <identifier type="doi">10.1007/s11548-006-0024-x</identifier>
    <author>Hans Lamecker</author>
    <author>Stefan Zachow</author>
    <author>Hans-Christian Hege</author>
    <author>Maja Zöckler</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="persons" number="lamecker">Lamecker, Hans</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="Cranio">Cranio</collection>
    <collection role="projects" number="MATHEON-F2">MATHEON-F2</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>3828</id>
    <completedYear>2006</completedYear>
    <publishedYear>2006</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>53</pageFirst>
    <pageLast>64</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>14(1)</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Computer assisted planning in cranio-maxillofacial surgery</title>
    <parentTitle language="eng">Journal of Computing and Information Technology</parentTitle>
    <author>Stefan Zachow</author>
    <author>Hans-Christian Hege</author>
    <author>Peter Deuflhard</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="deuflhard">Deuflhard, Peter</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>3829</id>
    <completedYear>2006</completedYear>
    <publishedYear>2006</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>415</pageFirst>
    <pageLast>417</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Is the course of the mandibular nerve deducible from the shape of the mandible?</title>
    <parentTitle language="eng">Int. J. of Computer Assisted Radiology and Surgery</parentTitle>
    <author>Stefan Zachow</author>
    <author>Hans Lamecker</author>
    <author>Barbara Elsholtz</author>
    <author>Michael Stiller</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="lamecker">Lamecker, Hans</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="Dental">Dental</collection>
    <collection role="projects" number="MATHEON-F2">MATHEON-F2</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>3830</id>
    <completedYear>2006</completedYear>
    <publishedYear>2006</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>165</pageFirst>
    <pageLast>167</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">CFD simulation of nasal airflow: Towards treatment planning for functional rhinosurgery</title>
    <parentTitle language="eng">Int. J. of Computer Assisted Radiology and Surgery</parentTitle>
    <author>Stefan Zachow</author>
    <author>Alexander Steinmann</author>
    <author>Thomas Hildebrandt</author>
    <author>Rainer Weber</author>
    <author>Werner Heppt</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="Rhino-CFD">Rhino-CFD</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>3944</id>
    <completedYear>2002</completedYear>
    <publishedYear>2002</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>111</pageFirst>
    <pageLast>116</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace>Heidelberg</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Visualisierung von Knochen und Weichteilveränderungen in der Distraktionsosteogenese des Mittelgesichtes</title>
    <parentTitle language="eng">Proc. 10. Jahrestagung der Deutschen Gesellschaft für Schädelbasischirurgie</parentTitle>
    <author>Thomas Hierl</author>
    <author>Gert Wollny</author>
    <author>Stefan Zachow</author>
    <author>Rainer Klöppel</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>4001</id>
    <completedYear>2000</completedYear>
    <publishedYear>2000</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>23</pageFirst>
    <pageLast>28</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Elsevier Science B.V.</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Finite-Element Simulation of Soft Tissue Deformation</title>
    <parentTitle language="eng">Computer Assisted Radiology and Surgey (CARS)</parentTitle>
    <author>Stefan Zachow</author>
    <author>Evgeny Gladilin</author>
    <author>Hans-Christian Hege</author>
    <author>Peter Deuflhard</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="deuflhard">Deuflhard, Peter</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>4022</id>
    <completedYear>1999</completedYear>
    <publishedYear>1999</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>942</pageFirst>
    <pageLast>946</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Elsevier Science B.V.</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Optimized Arrangement of Osseointegrated Implants: A Surgical Planning System for the Fixation of Facial Protheses</title>
    <parentTitle language="eng">Computer Assisted Radiology and Surgery (CARS’99)</parentTitle>
    <author>Stefan Zachow</author>
    <author>Tim Lueth</author>
    <author>Detlev Stalling</author>
    <author>Andreas Hein</author>
    <author>Martin Klein</author>
    <author>Horst Menneking</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>4023</id>
    <completedYear>1999</completedYear>
    <publishedYear>1999</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1049</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Elsevier Science B.V.</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A 2D Planning Sytem for Robot-Assisted Interventions</title>
    <parentTitle language="eng">Computer Assisted Radiology and Surgery</parentTitle>
    <author>Andreas Hein</author>
    <author>Tim Lueth</author>
    <author>Stefan Zachow</author>
    <author>Malte Stien</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>4024</id>
    <completedYear>1999</completedYear>
    <publishedYear>1999</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>203</pageFirst>
    <pageLast>207</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer-Verlag, Berlin</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Mehrschichtige Oberflächenmodelle zur computergestützten Planung in der Chirurgie</title>
    <parentTitle language="eng">Bildverarbeitung für die Medizin 1999 - Algorithmen, Anwendungen</parentTitle>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-5661</enrichment>
    <author>Detlev Stalling</author>
    <author>Martin Seebaß</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="visalgo">Visual Data Analysis in Science and Engineering</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>4145</id>
    <completedYear>2005</completedYear>
    <publishedYear>2005</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>doctoralthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation>Technische Universität Berlin</contributingCorporation>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Computer assisted osteotomy planning in cranio-maxillofacial surgery under consideration of facial soft tissue changes</title>
    <additionalTitle language="deu">Computergestützte Osteotomieplanung in der Mund-Kiefer-Gesichtschirurgie unter Berücksichtigung der räumlichen Weichgewebeanordnung</additionalTitle>
    <identifier type="url">www.dr.hut-verlag.de/titelMedizininformatik.html</identifier>
    <author>Stefan Zachow</author>
    <collection role="collections" number="">Dissertationen</collection>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>4249</id>
    <completedYear/>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2013-09-18</completedDate>
    <publishedDate>2013-09-18</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Visual Support for Positioning Hearing Implants</title>
    <abstract language="eng">We present a software planning tool that provides intuitive visual feedback for finding suitable positions of hearing implants in the human temporal bone. After an automatic reconstruction of the temporal bone anatomy the tool pre-positions the implant and allows the user to adjust its position interactively with simple 2D dragging and rotation operations on the bone's surface. During this procedure, visual elements like warning labels on the implant or color encoded bone density information on the bone geometry provide guidance for the determination of a suitable fit.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-42495</identifier>
    <enrichment key="PeerReviewed">no</enrichment>
    <enrichment key="SourceTitle">appeared in Proceedings of the 12th annual meeting of the CURAC society</enrichment>
    <author>Heiko Ramm</author>
    <submitter>Heiko Ramm</submitter>
    <author>Oscar Salvador Morillo Victoria</author>
    <author>Ingo Todt</author>
    <author>Hartmut Schirmacher</author>
    <author>Arneborg Ernst</author>
    <author>Stefan Zachow</author>
    <author>Hans Lamecker</author>
    <series>
      <title>ZIB-Report</title>
      <number>13-53</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>bone anchored hearing implant</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>surgery planning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>segmentation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>visualization</value>
    </subject>
    <collection role="ccs" number="J.">Computer Applications</collection>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="lamecker">Lamecker, Hans</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="BoneBridge">BoneBridge</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/4249/ZIB-Report 13-35 Visual support for the positioning of hearing implants.pdf</file>
  </doc>
  <doc>
    <id>7269</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2019-03-05</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Statistical Shape Models - Understanding and Mastering Variation in Anatomy</title>
    <abstract language="eng">In our chapter we are describing how to reconstruct three-dimensional anatomy from medical image data and how to build Statistical 3D Shape Models out of many such reconstructions yielding a new kind of anatomy that not only allows quantitative analysis of anatomical variation but also a visual exploration and educational visualization. Future digital anatomy atlases will not only show a static (average) anatomy but also its normal or pathological variation in three or even four dimensions, hence, illustrating growth and/or disease progression. Statistical Shape Models (SSMs) are geometric models that describe a collection of semantically similar objects in a very compact way. SSMs represent an average shape of many three-dimensional objects as well as their variation in shape. The creation of SSMs requires a correspondence mapping, which can be achieved e.g. by parameterization with a respective sampling. If a corresponding parameterization over all shapes can be established, variation between individual shape characteristics can be mathematically investigated. We will explain what Statistical Shape Models are and how they are constructed. Extensions of Statistical Shape Models will be motivated for articulated coupled structures. In addition to shape also the appearance of objects will be integrated into the concept. Appearance is a visual feature independent of shape that depends on observers or imaging techniques. Typical appearances are for instance the color and intensity of a visual surface of an object under particular lighting conditions, or measurements of material properties with computed tomography (CT) or magnetic resonance imaging (MRI). A combination of (articulated) statistical shape models with statistical models of appearance lead to articulated Statistical Shape and Appearance Models (a-SSAMs).After giving various examples of SSMs for human organs, skeletal structures, faces, and bodies, we will shortly describe clinical applications where such models have been successfully employed. Statistical Shape Models are the foundation for the analysis of anatomical cohort data, where characteristic shapes are correlated to demographic or epidemiologic data. SSMs consisting of several thousands of objects offer, in combination with statistical methods ormachine learning techniques, the possibility to identify characteristic clusters, thus being the foundation for advanced diagnostic disease scoring.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-72699</identifier>
    <identifier type="doi">10.1007/978-3-030-19385-0_5</identifier>
    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Hans Lamecker</author>
    <author>Christoph von Tycowicz</author>
    <author>Stefan Zachow</author>
    <series>
      <title>ZIB-Report</title>
      <number>19-13</number>
    </series>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="lamecker">Lamecker, Hans</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="BMBF-TOKMIS">BMBF-TOKMIS</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="persons" number="vontycowicz">Tycowicz, Christoph von</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="projects" number="ECMath-CH15">ECMath-CH15</collection>
    <collection role="projects" number="MathPlus-TrU-1">MathPlus-TrU-1</collection>
    <collection role="projects" number="MathPlus-EF2-3">MathPlus-EF2-3</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/7269/ZIB-Report.pdf</file>
  </doc>
  <doc>
    <id>7270</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2019-01-22</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative</title>
    <abstract language="eng">We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging (MRI) that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs).The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures.The shape models and neural networks employed are trained using data from the Osteoarthritis Initiative (OAI) and the MICCAI grand challenge "Segmentation of Knee Images 2010" (SKI10), respectively. We evaluate our method on 40 validation and 50 submission datasets from the SKI10 challenge.For the first time, an accuracy equivalent to the inter-observer variability of human readers is achieved in this challenge.Moreover, the quality of the proposed method is thoroughly assessed using various measures for data from the OAI, i.e. 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy for both OAI datasets. We make the 507 manual segmentations as well as our experimental setup publicly available to further aid research in the field of medical image segmentation.In conclusion, combining localized classification via CNNs with statistical anatomical knowledge via SSMs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-72704</identifier>
    <note>Innovation Excellence Award 2020</note>
    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Alexander Tack</author>
    <author>Moritz Ehlke</author>
    <author>Stefan Zachow</author>
    <series>
      <title>ZIB-Report</title>
      <number>19-06</number>
    </series>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="ehlke">Ehlke, Moritz</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="BMBF-TOKMIS">BMBF-TOKMIS</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/7270/ZIBReport_19-06.pdf</file>
  </doc>
  <doc>
    <id>7321</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>67</pageFirst>
    <pageLast>84</pageLast>
    <pageNumber/>
    <edition>1</edition>
    <issue>1156</issue>
    <volume>3</volume>
    <type>bookpart</type>
    <publisherName>Springer Nature Switzerland AG</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-07-24</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Statistical Shape Models - Understanding and Mastering Variation in Anatomy</title>
    <abstract language="eng">In our chapter we are describing how to reconstruct three-dimensional anatomy from medical image data and how to build Statistical 3D Shape Models out of many such reconstructions yielding a new kind of anatomy that not only allows quantitative analysis of anatomical variation but also a visual exploration and educational visualization. Future digital anatomy atlases will not only show a static (average) anatomy but also its normal or pathological variation in three or even four dimensions, hence, illustrating growth and/or disease progression. Statistical Shape Models (SSMs) are geometric models that describe a collection of semantically similar objects in a very compact way. SSMs represent an average shape of many three-dimensional objects as well as their variation in shape. The creation of SSMs requires a correspondence mapping, which can be achieved e.g. by parameterization with a respective sampling. If a corresponding parameterization over all shapes can be established, variation between individual shape characteristics can be mathematically investigated. We will explain what Statistical Shape Models are and how they are constructed. Extensions of Statistical Shape Models will be motivated for articulated coupled structures. In addition to shape also the appearance of objects will be integrated into the concept. Appearance is a visual feature independent of shape that depends on observers or imaging techniques. Typical appearances are for instance the color and intensity of a visual surface of an object under particular lighting conditions, or measurements of material properties with computed tomography (CT) or magnetic resonance imaging (MRI). A combination of (articulated) statistical shape models with statistical models of appearance lead to articulated Statistical Shape and Appearance Models (a-SSAMs).After giving various examples of SSMs for human organs, skeletal structures, faces, and bodies, we will shortly describe clinical applications where such models have been successfully employed. Statistical Shape Models are the foundation for the analysis of anatomical cohort data, where characteristic shapes are correlated to demographic or epidemiologic data. SSMs consisting of several thousands of objects offer, in combination with statistical methods ormachine learning techniques, the possibility to identify characteristic clusters, thus being the foundation for advanced diagnostic disease scoring.</abstract>
    <parentTitle language="eng">Biomedical Visualisation</parentTitle>
    <identifier type="isbn">978-3-030-19384-3</identifier>
    <identifier type="doi">10.1007/978-3-030-19385-0_5</identifier>
    <identifier type="isbn">978-3-030-19385-0</identifier>
    <enrichment key="Series">Advances in Experimental Medicine and Biology</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-72699</enrichment>
    <author>Felix Ambellan</author>
    <submitter>Stefan Zachow</submitter>
    <editor>Paul M. Rea</editor>
    <author>Hans Lamecker</author>
    <author>Christoph von Tycowicz</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="lamecker">Lamecker, Hans</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="BMBF-TOKMIS">BMBF-TOKMIS</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="persons" number="vontycowicz">Tycowicz, Christoph von</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="projects" number="ECMath-CH15">ECMath-CH15</collection>
    <collection role="projects" number="MathPlus-TrU-1">MathPlus-TrU-1</collection>
    <collection role="projects" number="MathPlus-EF2-3">MathPlus-EF2-3</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="institutes" number="GDAP">Geometric Data Analysis and Processing</collection>
  </doc>
  <doc>
    <id>7239</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>9</pageFirst>
    <pageLast>13</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>35</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-02-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The Healthy Nasal Cavity - Characteristics of Morphology and Related Airflow Based on a Statistical Shape Model Viewed from a Surgeon’s Perspective</title>
    <abstract language="eng">Functional surgery on the nasal framework requires referential criteria to objectively assess nasal breathing for indication and follow-up. Thismotivated us to generate amean geometry of the nasal cavity based on a statistical shape model. In this study, the authors could demonstrate that the introduced nasal cavity’s mean geometry features characteristics of the inner shape and airflow, which are commonly observed in symptom-free subjects. Therefore, the mean geometry might serve as a reference-like model when one considers qualitative aspects. However, to facilitate quantitative considerations and statistical inference, further research is necessary. Additionally, the authorswere able to obtain details about the importance of the isthmus nasi and the inferior turbinate for the intranasal airstream.</abstract>
    <parentTitle language="eng">Facial Plastic Surgery</parentTitle>
    <identifier type="doi">10.1055/s-0039-1677721</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2018-11-25</enrichment>
    <author>Thomas Hildebrandt</author>
    <submitter>Stefan Zachow</submitter>
    <author>Jan Joris Bruening</author>
    <author>Nora Laura Schmidt</author>
    <author>Hans Lamecker</author>
    <author>Werner Heppt</author>
    <author>Stefan Zachow</author>
    <author>Leonid Goubergrits</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>statistical shape model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>nasal cavity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>nasal breathing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>nasal airflow</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>isthmus nasi</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>inferior turbinate</value>
    </subject>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="lamecker">Lamecker, Hans</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Rhino-CFD">Rhino-CFD</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>7240</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>8</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>35</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-02-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Digital Analysis of Nasal Airflow Facilitating Decision Support in Rhinosurgery</title>
    <abstract language="eng">Successful functional surgery on the nasal framework requires reliable and comprehensive diagnosis. In this regard, the authors introduce a new methodology: Digital Analysis of Nasal Airflow (diANA). It is based on computational fluid dynamics, a statistical shape model of the healthy nasal cavity and rhinologic expertise. diANA necessitates an anonymized tomographic dataset of the paranasal sinuses including the complete nasal cavity and, when available, clinical information. The principle of diANA is to compare the morphology and the respective airflow of an individual nose with those of a reference. This enablesmorphometric aberrations and consecutive flow field anomalies to localize and quantify within a patient’s nasal cavity. Finally, an elaborated expert opinion with instructive visualizations is provided. Using diANA might support surgeons in decision-making, avoiding unnecessary surgery, gaining more precision, and target-orientation for indicated operations.</abstract>
    <parentTitle language="eng">Facial Plastic Surgery</parentTitle>
    <identifier type="doi">10.1055/s-0039-1677720</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2018-11-18</enrichment>
    <author>Thomas Hildebrandt</author>
    <submitter>Stefan Zachow</submitter>
    <author>Jan Joris Bruening</author>
    <author>Hans Lamecker</author>
    <author>Stefan Zachow</author>
    <author>Werner Heppt</author>
    <author>Nora Schmidt</author>
    <author>Leonid Goubergrits</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>nasal airflow simulation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>nasal breathing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>statistical shape model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>diANA</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>nasal obstruction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>rhinorespiratory homeostasis</value>
    </subject>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="lamecker">Lamecker, Hans</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Rhino-CFD">Rhino-CFD</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>7121</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>40</pageFirst>
    <pageLast>43</pageLast>
    <pageNumber>4</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Accurate Automated Volumetry of Cartilage of the Knee using Convolutional Neural Networks: Data from the Osteoarthritis Initiative</title>
    <abstract language="eng">Volumetry  of  cartilage  of  the  knee  is  needed  for  knee  osteoarthritis (KOA) assessment. It is typically performed manually  in  a  tedious  and  subjective  process.   We  developed  a method for an automated, segmentation-based quantification of cartilage volume by employing 3D Convolutional Neural Networks (CNNs).  CNNs were trained in a supervised manner using magnetic resonance imaging data and cartilage volumetry readings performed by clinical experts for 1378 subjects provided by the Osteoarthritis Initiative.  It was shown that 3D CNNs are able to achieve volume measures comparable to the magnitude of variation between expert readings and&#13;
the real in vivo situation.  In the future, accurate automated cartilage volumetry might support both, diagnosis of KOA as well as longitudinal analysis of KOA progression.</abstract>
    <parentTitle language="deu">IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)</parentTitle>
    <identifier type="doi">10.1109/ISBI.2019.8759201</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2018-12-18</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-71439</enrichment>
    <author>Alexander Tack</author>
    <submitter>Alexander Tack</submitter>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>6995</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>109</pageFirst>
    <pageLast>118</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>52</volume>
    <type>researchdata</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative (Supplementary Material)</title>
    <abstract language="eng">We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures. The shape models and neural networks employed are trained using data of the Osteoarthritis Initiative (OAI) and the MICCAI grand challenge "Segmentation of Knee Images 2010" (SKI10), respectively. We evaluate our method on 40 validation and 50 submission datasets of the SKI10 challenge. For the first time, an accuracy equivalent to the inter-observer variability of human readers has been achieved in this challenge. Moreover, the quality of the proposed method is thoroughly assessed using various measures for data from the OAI, i.e. 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy for both OAI datasets. We made the 507 manual segmentations as well as our experimental setup publicly available to further aid research in the field of medical image segmentation. In conclusion, combining statistical anatomical knowledge via SSMs with the localized classification via CNNs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data.</abstract>
    <parentTitle language="eng">Medical Image Analysis</parentTitle>
    <identifier type="doi">10.12752/4.ATEZ.1.0</identifier>
    <note>OAI-ZIB dataset</note>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="SubmissionStatus">OAI-ZIB dataset</enrichment>
    <enrichment key="ScientificResourceTypeGeneral">Image</enrichment>
    <enrichment key="ScientificGeolocation">Berlin, Germany</enrichment>
    <enrichment key="zib_DownloadUrl">http://www.zib.de/ext-data/2019_ATEZ_MEDIA-Supplementary-Material-OAI-ZIB.zip</enrichment>
    <enrichment key="zib_relatedIdentifier">https://doi.org/10.12752/4.ATEZ.1.0</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Alexander Tack</author>
    <submitter>Alexander Tack</submitter>
    <author>Moritz Ehlke</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="ehlke">Ehlke, Moritz</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="BMBF-TOKMIS">BMBF-TOKMIS</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>7093</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>109</pageFirst>
    <pageLast>118</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>52</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-11-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative</title>
    <abstract language="eng">We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures. The shape models and neural networks employed are trained using data of the Osteoarthritis Initiative (OAI) and the MICCAI grand challenge "Segmentation of Knee Images 2010" (SKI10), respectively. We evaluate our method on 40 validation and 50 submission datasets of the SKI10 challenge. For the first time, an accuracy equivalent to the inter-observer variability of human readers has been achieved in this challenge. Moreover, the quality of the proposed method is thoroughly assessed using various measures for data from the OAI, i.e. 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy for both OAI datasets. We made the 507 manual segmentations as well as our experimental setup publicly available to further aid research in the field of medical image segmentation. In conclusion, combining statistical anatomical knowledge via SSMs with the localized classification via CNNs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data.</abstract>
    <parentTitle language="eng">Medical Image Analysis</parentTitle>
    <identifier type="doi">10.1016/j.media.2018.11.009</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-72704</enrichment>
    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Alexander Tack</author>
    <author>Moritz Ehlke</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="ehlke">Ehlke, Moritz</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="BMBF-TOKMIS">BMBF-TOKMIS</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>7097</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>24</pageFirst>
    <pageLast>41</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>52</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-11-16</completedDate>
    <publishedDate>2019-02-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">CATARACTS: Challenge on Automatic Tool Annotation for cataRACT Surgery</title>
    <abstract language="eng">Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future.</abstract>
    <parentTitle language="eng">Medical Image Analysis</parentTitle>
    <identifier type="doi">10.1016/j.media.2018.11.008</identifier>
    <note>Best paper award - Computer Graphics Night 2020 (TU Darmstadt)</note>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2018-11-15</enrichment>
    <author>Hassan Al Hajj</author>
    <submitter>Manish Sahu</submitter>
    <author>Manish Sahu</author>
    <author>Mathieu Lamard</author>
    <author>Pierre-Henri Conze</author>
    <author>Soumali Roychowdhury</author>
    <author>Xiaowei Hu</author>
    <author>Gabija Marsalkaite</author>
    <author>Odysseas Zisimopoulos</author>
    <author>Muneer Ahmad Dedmari</author>
    <author>Fenqiang Zhao</author>
    <author>Jonas Prellberg</author>
    <author>Adrian Galdran</author>
    <author>Teresa Araujo</author>
    <author>Duc My Vo</author>
    <author>Chandan Panda</author>
    <author>Navdeep Dahiya</author>
    <author>Satoshi Kondo</author>
    <author>Zhengbing Bian</author>
    <author>Jonas Bialopetravicius</author>
    <author>Chenghui Qiu</author>
    <author>Sabrina Dill</author>
    <author>Anirban Mukhopadyay</author>
    <author>Pedro Costa</author>
    <author>Guilherme Aresta</author>
    <author>Senthil Ramamurthy</author>
    <author>Sang-Woong Lee</author>
    <author>Aurelio Campilho</author>
    <author>Stefan Zachow</author>
    <author>Shunren Xia</author>
    <author>Sailesh Conjeti</author>
    <author>Jogundas Armaitis</author>
    <author>Pheng-Ann Heng</author>
    <author>Arash Vahdat</author>
    <author>Beatrice Cochener</author>
    <author>Gwenole Quellec</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="BMBF-BiOPAss">BMBF-BiOPAss</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>7143</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-01-24</completedDate>
    <publishedDate>2019-01-24</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Accurate Automated Volumetry of Cartilage of the Knee using Convolutional Neural Networks: Data from the Osteoarthritis Initiative</title>
    <abstract language="eng">Volumetry of the cartilage of the knee, as needed for the assessment of knee osteoarthritis (KOA), is typically performed in a tedious and subjective process. We present an automated segmentation-based method for the quantification of cartilage volume by employing 3D Convolutional Neural Networks&#13;
(CNNs). CNNs were trained in a supervised manner using magnetic resonance imaging data as well as cartilage volumetry readings given by clinical experts for 1378 subjects. It was shown that 3D CNNs can be employed for cartilage volumetry with an accuracy similar to expert volumetry readings. In future, accurate automated cartilage volumetry might support both, diagnosis of KOA as well as assessment of KOA progression via longitudinal analysis.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-71439</identifier>
    <author>Alexander Tack</author>
    <submitter>Alexander Tack</submitter>
    <author>Stefan Zachow</author>
    <series>
      <title>ZIB-Report</title>
      <number>19-05</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>imaging biomarker</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>radiomics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cartilage morphometry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>volume assessment</value>
    </subject>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/7143/zibreport_isbi_2019.pdf</file>
  </doc>
  <doc>
    <id>7083</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>127</pageFirst>
    <pageLast>144</pageLast>
    <pageNumber/>
    <edition/>
    <issue>12</issue>
    <volume>142</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-10-25</completedDate>
    <publishedDate>2018-12-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Traumatic events in the life of the deep-sea cephalopod mollusc, the coleoid Spirula spirula</title>
    <abstract language="eng">Here, we report on different types of shell pathologies of the enigmatic deep-sea (mesopelagic) cephalopod Spirula spirula. For the first time, we apply non-invasive imaging methods to: document trauma-induced changes in shell shapes, reconstruct the different causes and effects of these pathologies, unravel the etiology, and attempt to quantify the efficiency of the buoyancy apparatus. We have analysed 2D and 3D shell parameters from eleven shells collected as beach findings from the Canary Islands (Gran Canaria and Fuerteventura), West-Australia, and the Maldives. All shells were scanned with a nanotom-m computer tomograph. Seven shells were likely injured by predator attacks: fishes, cephalopods or crustaceans, one specimen was infested by an endoparasite (potentially Digenea) and one shell shows signs of inflammation and one shell shows large fluctuations of chamber volumes without any signs of pathology. These fluctuations are potential indicators of a stressed environment. Pathological shells represent the most deviant morphologies of a single species and can therefore be regarded as morphological end-members. The changes in the shell volume / chamber volume ratio were assessed in order to evaluate the functional tolerance of the buoyancy apparatus showing that these had little effect.</abstract>
    <parentTitle language="eng">ScienceDirect: Deep Sea Research Part I - Oceanographic Research</parentTitle>
    <identifier type="doi">10.1016/j.dsr.2018.10.007</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">25.10.2018</enrichment>
    <author>Rene Hoffmann</author>
    <submitter>Stefan Zachow</submitter>
    <author>Robert Lemanis</author>
    <author>Lena Wulff</author>
    <author>Stefan Zachow</author>
    <author>Alexander Lukeneder</author>
    <author>Christian Klug</author>
    <author>Helmut Keupp</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="DFG-SeptCom-Ammonoids">DFG-SeptCom-Ammonoids</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>6117</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2016-11-29</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Riemannian Statistical Shape Model using Differential Coordinates</title>
    <abstract language="eng">We propose a novel Riemannian framework for statistical analysis of shapes that is able to account for the nonlinearity in shape variation. By adopting a physical perspective, we introduce a differential representation that puts the local geometric variability into focus. We model these differential coordinates as elements of a Lie group thereby endowing our shape space with a non-Euclidian structure. A key advantage of our framework is that statistics in a manifold shape space become numerically tractable improving performance by several orders of magnitude over state-of-the-art. We show that our Riemannian model is well suited for the identification of intra-population variability as well as inter-population differences. In particular, we demonstrate the superiority of the proposed model in experiments on specificity and generalization ability. We further derive a statistical shape descriptor that outperforms the standard Euclidian approach in terms of shape-based classification of morphological disorders.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-61175</identifier>
    <identifier type="url">https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/6485</identifier>
    <author>Christoph von Tycowicz</author>
    <submitter>Felix Ambellan</submitter>
    <author>Felix Ambellan</author>
    <author>Anirban Mukhopadhyay</author>
    <author>Stefan Zachow</author>
    <series>
      <title>ZIB-Report</title>
      <number>16-69</number>
    </series>
    <collection role="ccs" number="">Statistical computing</collection>
    <collection role="ccs" number="I.3.5">Computational Geometry and Object Modeling</collection>
    <collection role="msc" number="53-04">Explicit machine computation and programs (not the theory of computation or programming)</collection>
    <collection role="msc" number="62-04">Explicit machine computation and programs (not the theory of computation or programming)</collection>
    <collection role="msc" number="68U05">Computer graphics; computational geometry [See also 65D18]</collection>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="BMBF-TOKMIS">BMBF-TOKMIS</collection>
    <collection role="projects" number="DFG-Knee-Laxity">DFG-Knee-Laxity</collection>
    <collection role="persons" number="vontycowicz">Tycowicz, Christoph von</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="ECMath-CH15">ECMath-CH15</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/6117/ZIBReport_16-69.pdf</file>
  </doc>
  <doc>
    <id>6131</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>e2434</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>4</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2016-09-12</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Comparative cephalopod shell strength and the role of septum morphology on stress distribution</title>
    <abstract language="eng">The evolution of complexly folded septa in ammonoids has long been a controversial topic. Explanations of the function of these folded septa can be divided into physiological and mechanical hypotheses with the mechanical functions tending to find widespread support. The complexity of the cephalopod shell has made it difficult to directly test the mechanical properties of these structures without oversimplification of the septal morphology or extraction of a small sub-domain. However, the power of modern finite element analysis now permits direct testing of mechanical hypothesis on complete, empirical models of the shells taken from computed tomographic data. Here we compare, for the first time using empirical models, the capability of the shells of extant Nautilus pompilius, Spirula spirula, and the extinct ammonite Cadoceras sp. to withstand hydrostatic pressure and point loads. Results show hydrostatic pressure imparts highest stress on the final septum with the rest of the shell showing minimal compression. S. spirula shows the lowest stress under hydrostatic pressure while N. pompilius shows the highest stress. Cadoceras sp. shows the development of high stress along the attachment of the septal saddles with the shell wall. Stress due to point loads decreases when the point force is directed along the suture as opposed to the unsupported chamber wall. Cadoceras sp. shows the greatest decrease in stress between the point loads compared to all other models. Greater amplitude of septal flutes corresponds with greater stress due to hydrostatic pressure; however, greater amplitude decreases the stress magnitude of point loads directed along the suture. In our models, sutural complexity does not predict greater resistance to hydrostatic pressure but it does seem to increase resistance to point loads, such as would be from predators. This result permits discussion of palaeoecological reconstructions on the basis of septal morphology. We further suggest that the ratio used to characterize septal morphology in the septal strength index and in calculations of tensile strength of nacre are likely insufficient. A better understanding of the material properties of cephalopod nacre may allow the estimation of maximum depth limits of shelled cephalopods through finite element analysis.</abstract>
    <parentTitle language="eng">PeerJ</parentTitle>
    <identifier type="doi">10.7717/peerj.2434</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Robert Lemanis</author>
    <submitter>Stefan Zachow</submitter>
    <author>Stefan Zachow</author>
    <author>René Hoffmann</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="DFG-SeptCom-Ammonoids">DFG-SeptCom-Ammonoids</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>6198</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>77</pageFirst>
    <pageLast>89</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>38</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2017-02-14</completedDate>
    <publishedDate>2017-05-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds</title>
    <abstract language="eng">The reconstruction of an object’s shape or surface from a set of 3D points plays an important role in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or in the process of aligning intra-operative navigation and preoperative planning data. In such scenarios, one usually has to deal with sparse data, which significantly aggravates the problem of reconstruction. However, medical applications often provide contextual information about the 3D point data that allow to incorporate prior knowledge about the shape that is to be reconstructed. To this end, we propose the use of a statistical shape model (SSM) as a prior for surface reconstruction. The SSM is represented by a point distribution model (PDM), which is associated with a surface mesh. Using the shape distribution that is modelled by the PDM, we formulate the problem of surface reconstruction from a probabilistic perspective based on a Gaussian Mixture Model (GMM). In order to do so, the given points are interpreted as samples of the GMM. By using mixture components with anisotropic covariances that are “oriented” according to the surface normals at the PDM points, a surface-based fitting is accomplished. Estimating the parameters of the GMM in a maximum a posteriori manner yields the reconstruction of the surface from the given data points. We compare our method to the extensively used Iterative Closest Points method on several different anatomical datasets/SSMs (brain, femur, tibia, hip, liver) and demonstrate superior accuracy and robustness on sparse data.</abstract>
    <parentTitle language="eng">Medical Image Analysis</parentTitle>
    <identifier type="doi">10.1016/j.media.2017.02.005</identifier>
    <identifier type="url">http://www.sciencedirect.com/science/article/pii/S1361841517300233</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="FulltextUrl">https://arxiv.org/pdf/1602.08425.pdf</enrichment>
    <author>Florian Bernard</author>
    <submitter>Alexander Tack</submitter>
    <author>Luis Salamanca</author>
    <author>Johan Thunberg</author>
    <author>Alexander Tack</author>
    <author>Dennis Jentsch</author>
    <author>Hans Lamecker</author>
    <author>Stefan Zachow</author>
    <author>Frank Hertel</author>
    <author>Jorge Goncalves</author>
    <author>Peter Gemmar</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="lamecker">Lamecker, Hans</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>2591</id>
    <completedYear>2008</completedYear>
    <publishedYear>2008</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>140</pageFirst>
    <pageLast>156</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>bookpart</type>
    <publisherName>Health Academy</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Modellgestützte Operationsplanung in der Kopfchirurgie</title>
    <parentTitle language="eng">Modellgestützte Therapie</parentTitle>
    <author>Stefan Zachow</author>
    <editor>Wolfgang Niederlag</editor>
    <author>Martin Weiser</author>
    <editor>Heinz Lemke</editor>
    <author>Peter Deuflhard</author>
    <editor>Jürgen Meixensberger</editor>
    <editor>Michael Baumann</editor>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="institutes" number="compmed">Computational Medicine</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="deuflhard">Deuflhard, Peter</collection>
    <collection role="persons" number="weiser">Weiser, Martin</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="FacialSurgery">FacialSurgery</collection>
  </doc>
  <doc>
    <id>2592</id>
    <completedYear>2000</completedYear>
    <publishedYear>2000</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>82</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>28 (Suppl. 1)</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Treatment Planning and Simulation in Craniofacial Surgery with Virtual Reality Techiques</title>
    <parentTitle language="eng">Journal of Cranio-Maxillofacial Surgery</parentTitle>
    <author>Hans-Florian Zeilhofer</author>
    <author>Stefan Zachow</author>
    <author>Jeffrey Fairley</author>
    <author>Robert Sader</author>
    <author>Peter Deuflhard</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="deuflhard">Deuflhard, Peter</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
  </doc>
  <doc>
    <id>4405</id>
    <completedYear>2013</completedYear>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>15</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Springer Berlin Heidelberg</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">GPU-accelerated level-set segmentation</title>
    <parentTitle language="eng">Journal of Real-Time Image Processing</parentTitle>
    <identifier type="doi">10.1007/s11554-013-0378-6</identifier>
    <identifier type="url">http://dx.doi.org/10.1007/s11554-013-0378-6</identifier>
    <identifier type="issn">1861-8200</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Julián Lamas-Rodríguez</author>
    <author>Dora Blanco Heras</author>
    <author>Francisco Argüello</author>
    <author>Dagmar Kainmüller</author>
    <author>Stefan Zachow</author>
    <author>Montserrat Bóo</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>4406</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2721</pageFirst>
    <pageLast>2739</pageLast>
    <pageNumber/>
    <edition/>
    <issue>10</issue>
    <volume>11</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Non-invasive imaging methods applied to neo- and paleontological cephalopod research</title>
    <abstract language="eng">Several non-invasive methods are common practice in natural sciences today. Here we present how they can be applied and contribute to current topics in cephalopod (paleo-) biology. Different methods will be compared in terms of time necessary to acquire the data, amount of data, accuracy/resolution, minimum/maximum size of objects that can be studied, the degree of post-processing needed and availability. The main application of the methods is seen in morphometry and volumetry of cephalopod shells. In particular we present a method for precise buoyancy calculation. Therefore, cephalopod shells were scanned together with different reference bodies, an approach developed in medical sciences. It is necessary to know the volume of the reference bodies, which should have similar absorption properties like the object of interest. Exact volumes can be obtained from surface scanning. Depending on the dimensions of the study object different computed tomography techniques were applied.</abstract>
    <parentTitle language="eng">Biogeosciences</parentTitle>
    <identifier type="doi">10.5194/bg-11-2721-2014</identifier>
    <note>To access the corresponding discussion paper go to www.biogeosciences-discuss.net/10/18803/2013/ - Biogeosciences Discuss., 10, 18803-18851, 2013</note>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-50300</enrichment>
    <author>René Hoffmann</author>
    <author>Julia A. Schultz</author>
    <author>Rico Schellhorn</author>
    <author>Erik Rybacki</author>
    <author>Helmut Keupp</author>
    <author>S. R. Gerden</author>
    <author>Robert Lemanis</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="DFG-SeptCom-Ammonoids">DFG-SeptCom-Ammonoids</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>4403</id>
    <completedYear>2013</completedYear>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>116</pageFirst>
    <pageLast>120</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Visual Support for Positioning Hearing Implants</title>
    <parentTitle language="eng">Proceedings of the 12th annual meeting of the CURAC society</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-42495</enrichment>
    <author>Heiko Ramm</author>
    <editor>Wolfgang Freysinger</editor>
    <author>Oscar Salvador Victoria Morillo</author>
    <author>Ingo Todt</author>
    <author>Hartmut Schirmacher</author>
    <author>Arneborg Ernst</author>
    <author>Stefan Zachow</author>
    <author>Hans Lamecker</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="lamecker">Lamecker, Hans</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="BoneBridge">BoneBridge</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>4404</id>
    <completedYear>2013</completedYear>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>429</pageFirst>
    <pageLast>441</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>17</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Omnidirectional Displacements for Deformable Surfaces</title>
    <parentTitle language="eng">Medical Image Analysis</parentTitle>
    <identifier type="doi">10.1016/j.media.2012.11.006</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Dagmar Kainmüller</author>
    <author>Hans Lamecker</author>
    <author>Markus O. Heller</author>
    <author>Britta Weber</author>
    <author>Hans-Christian Hege</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="visalgo">Visual Data Analysis in Science and Engineering</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="persons" number="lamecker">Lamecker, Hans</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>5978</id>
    <completedYear>2016</completedYear>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>16</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2016-10-10</completedDate>
    <publishedDate>2016-10-10</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Microtomography of the Baltic amber tick Ixodes succineus reveals affinities with the modern Asian disease vector Ixodes ovatus</title>
    <abstract language="eng">Background: Fossil ticks are extremely rare, whereby Ixodes succineus Weidner, 1964 from Eocene (ca. 44-49 Ma) Baltic amber is one of the oldest examples of a living hard tick genus (Ixodida: Ixodidae). Previous work suggested it was most closely related to the modern and widespread European sheep tick Ixodes ricinus (Linneaus, 1758).&#13;
&#13;
Results: Restudy using phase contrast synchrotron x-ray tomography yielded images of exceptional quality. These confirm the fossil's referral to Ixodes Latreille, 1795, but the characters resolved here suggest instead affinities with the Asian subgenus Partipalpiger Hoogstraal et al., 1973 and its single living (and medically significant) species Ixodes ovatus Neumann, 1899. We redescribe the amber fossil here as Ixodes (Partipalpiger) succineus.&#13;
&#13;
Conclusions: Our data suggest that Ixodes ricinus is unlikely to be directly derived from Weidner's amber species, but instead reveals that the Partipalpiger lineage was originally more widely distributed across the northern hemisphere. The closeness of Ixodes (P.) succineus to a living vector of a wide range of pathogens offers the potential to correlate its spatial and temporal position (northern Europe, nearly 50 million years ago) with the estimated origination dates of various tick-borne diseases.</abstract>
    <parentTitle language="eng">BMC Evolutionary Biology</parentTitle>
    <identifier type="doi">10.1186/s12862-016-0777-y</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="SourceTitle">BMC Evolutionary Biology</enrichment>
    <author>Jason Dunlop</author>
    <submitter>Moritz Ehlke</submitter>
    <author>Dmitry Apanaskevich</author>
    <author>Jens Lehmann</author>
    <author>Rene Hoffmann</author>
    <author>Florian Fusseis</author>
    <author>Moritz Ehlke</author>
    <author>Stefan Zachow</author>
    <author>Xianghui Xiao</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="ehlke">Ehlke, Moritz</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="DFG-SeptCom-Ammonoids">DFG-SeptCom-Ammonoids</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>5957</id>
    <completedYear>2017</completedYear>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>699</pageFirst>
    <pageLast>712</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>131</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2017-01-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automatic CT-based finite element model generation for temperature-based death time estimation: feasibility study and sensitivity analysis</title>
    <abstract language="eng">Temperature based death time estimation is based either on simple phenomenological models of corpse cooling or on detailed physical heat transfer models. The latter are much more complex, but allow a higher accuracy of death time estimation as in principle all relevant cooling mechanisms can be taken into account. Here, a complete work flow for finite element based cooling simulation models is presented.&#13;
&#13;
The following steps are demonstrated on CT-phantoms:&#13;
• CT-scan&#13;
• Segmentation of the CT images for thermodynamically relevant features of individual&#13;
geometries&#13;
• Conversion of the segmentation result into a Finite Element (FE) simulation model&#13;
• Computation of the model cooling curve&#13;
• Calculation of the cooling time&#13;
&#13;
For the first time in FE-based cooling time estimation the steps from the CT image over segmentation to FE model generation are semi-automatically performed. The cooling time calculation results are compared to cooling measurements performed on the phantoms under controlled conditions. In this context, the method is validated using different CTphantoms. Some of the CT phantoms thermodynamic material parameters had to be experimentally determined via independent experiments. Moreover the impact of geometry and material parameter uncertainties on the estimated cooling time is investigated by a sensitivity analysis.</abstract>
    <parentTitle language="eng">International Journal of Legal Medicine</parentTitle>
    <identifier type="doi">doi:10.1007/s00414-016-1523-0</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <submitter>Bodo Erdmann</submitter>
    <author>Sebastian Schenkl</author>
    <author>Holger Muggenthaler</author>
    <author>Michael Hubig</author>
    <author>Bodo Erdmann</author>
    <author>Martin Weiser</author>
    <author>Stefan Zachow</author>
    <author>Andreas Heinrich</author>
    <author>Felix Victor Güttler</author>
    <author>Ulf Teichgräber</author>
    <author>Gita Mall</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>temperature based death time estimation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>finite element method</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>CT segmentation</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>sensitivity analysis</value>
    </subject>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="compmed">Computational Medicine</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="weiser">Weiser, Martin</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="UJena-Forensic">UJena-Forensic</collection>
    <collection role="projects" number="ZIB-Kaskade7">ZIB-Kaskade7</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>6532</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>106</pageFirst>
    <pageLast>111</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>16</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Computerassistierte Auswahl und Platzierung von interpositionalen Spacern zur Behandlung früher Gonarthrose</title>
    <abstract language="deu">Degenerative Gelenkerkrankungen, wie die Osteoarthrose, sind ein häufiges Krankheitsbild unter älteren Erwachsenen. Hierbei verringert sich u.a. der Gelenkspalt aufgrund degenerierten Knorpels oder geschädigter Menisci. Ein in den Gelenkspalt eingebrachter interpositionaler Spacer soll die mit der Osteoarthrose einhergehende verringerte Gelenkkontaktfläche erhöhen und so der teilweise oder vollständige Gelenkersatz hinausgezögert oder vermieden werden.&#13;
&#13;
In dieser Arbeit präsentieren wir eine Planungssoftware für die Auswahl und Positionierung eines interpositionalen Spacers am Patientenmodell.&#13;
&#13;
Auf einer MRT-basierten Bildsegmentierung aufbauend erfolgt eine geometrische Rekonstruktion der 3D-Anatomie des Kniegelenks. Anhand dieser wird der Gelenkspalt bestimmt, sowie ein Spacer ausgewählt und algorithmisch vorpositioniert. Die Positionierung des Spacers ist durch den Benutzer jederzeit interaktiv anpassbar.&#13;
&#13;
Für jede Positionierung eines Spacers wird ein Fitness-Wert zur Knieanatomie des jeweiligen Patienten berechnet und den Nutzern Rückmeldung hinsichtlich Passgenauigkeit gegeben. Die Software unterstützt somit als Entscheidungshilfe die behandelnden Ärzte bei der patientenspezifischen Spacerauswahl.</abstract>
    <parentTitle language="deu">Proceedings of the Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC)</parentTitle>
    <identifier type="urn">urn:nbn:de:0297-zib-65321</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-66064</enrichment>
    <author>Robert Joachimsky</author>
    <submitter>Robert Joachimsky</submitter>
    <author>Felix Ambellan</author>
    <author>Stefan Zachow</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Osteoarthrose</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Kniegelenk</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Medizinische Planung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Visualisierung</value>
    </subject>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="BMBF-TOKMIS">BMBF-TOKMIS</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>6533</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>24</pageFirst>
    <pageLast>30</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>16</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Evaluating two methods for Geometry Reconstruction from Sparse Surgical Navigation Data</title>
    <abstract language="eng">In this study we investigate methods for fitting a Statistical Shape Model (SSM) to intraoperatively acquired point cloud data from a surgical navigation system. We validate the fitted models against the pre-operatively acquired Magnetic Resonance Imaging (MRI) data from the same patients.&#13;
&#13;
We consider a cohort of 10 patients who underwent navigated total knee arthroplasty. As part of the surgical protocol the patients’ distal femurs were partially digitized. All patients had an MRI scan two months pre-operatively. The MRI data were manually segmented and the reconstructed bone surfaces used as ground truth against which the fit was compared. Two methods were used to fit the SSM to the data, based on (1) Iterative Closest Points (ICP) and (2) Gaussian Mixture Models (GMM).&#13;
&#13;
For both approaches, the difference between model fit and ground truth surface averaged less than 1.7 mm and excellent correspondence with the distal femoral morphology can be demonstrated.</abstract>
    <parentTitle language="eng">Proceedings of the Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC)</parentTitle>
    <identifier type="urn">urn:nbn:de:0297-zib-65339</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-66052</enrichment>
    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Alexander Tack</author>
    <author>Dave Wilson</author>
    <author>Carolyn Anglin</author>
    <author>Hans Lamecker</author>
    <author>Stefan Zachow</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Total Knee Arthoplasty</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Sparse Geometry Reconstruction</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Statistical Shape Models</value>
    </subject>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="lamecker">Lamecker, Hans</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="BMBF-TOKMIS">BMBF-TOKMIS</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>6605</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2017-10-05</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Evaluating two methods for Geometry Reconstruction from Sparse Surgical Navigation Data</title>
    <abstract language="eng">In this study we investigate methods for fitting a Statistical Shape Model (SSM) to intraoperatively acquired point cloud data from a surgical navigation system. We validate the fitted models against the pre-operatively acquired Magnetic Resonance Imaging (MRI) data from the same patients.&#13;
&#13;
We consider a cohort of 10 patients who underwent navigated total knee arthroplasty. As part of the surgical protocol the patients’ distal femurs were partially digitized. All patients had an MRI scan two months pre-operatively. The MRI data were manually segmented and the reconstructed bone surfaces used as ground truth against which the fit was compared. Two methods were used to fit the SSM to the data, based on (1) Iterative Closest Points (ICP) and (2) Gaussian Mixture Models (GMM).&#13;
&#13;
For both approaches, the difference between model fit and ground truth surface averaged less than 1.7 mm and excellent correspondence with the distal femoral morphology can be demonstrated.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-66052</identifier>
    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Alexander Tack</author>
    <author>Dave Wilson</author>
    <author>Carolyn Anglin</author>
    <author>Hans Lamecker</author>
    <author>Stefan Zachow</author>
    <series>
      <title>ZIB-Report</title>
      <number>17-71</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Knee Arthroplasty</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sparse Geometry Reconstruction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Statistical Shape Models</value>
    </subject>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="BMBF-TOKMIS">BMBF-TOKMIS</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/6605/ZIB-Report-17-71.pdf</file>
  </doc>
  <doc>
    <id>6911</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2017-11-30</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Surgical Tool Presence Detection for Cataract Procedures</title>
    <abstract language="eng">This article outlines the submission to the CATARACTS challenge for automatic tool presence detection [1]. Our approach for this multi-label classification problem comprises labelset-based sampling, a CNN architecture and temporal smothing as described in [3], which we call ZIB-Res-TS.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-69110</identifier>
    <author>Manish Sahu</author>
    <submitter>Manish Sahu</submitter>
    <author>Sabrina Dill</author>
    <author>Anirban Mukhopadyay</author>
    <author>Stefan Zachow</author>
    <series>
      <title>ZIB-Report</title>
      <number>18-28</number>
    </series>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="BMBF-BiOPAss">BMBF-BiOPAss</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/6911/[ZIB Report] Surgical Tool Presence Detection for Cataract Procedures.pdf</file>
  </doc>
  <doc>
    <id>6747</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>680</pageFirst>
    <pageLast>688</pageLast>
    <pageNumber/>
    <edition/>
    <issue>5</issue>
    <volume>26</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-03-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Knee Menisci Segmentation using Convolutional Neural Networks: Data from the Osteoarthritis Initiative</title>
    <abstract language="eng">Abstract: Objective: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof.&#13;
Method: A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain. Correlation between computed meniscal extrusion and MOAKS experts' readings was evaluated for 600 subjects. Suitability of biomarkers for predicting incident radiographic OA from baseline to 24 months was tested on a group of 552 patients (184 incident OA, 386 controls) by performing conditional logistic regression.&#13;
Results: Segmentation accuracy measured as Dice Similarity Coefficient was 83.8% for medial menisci (MM) and 88.9% for lateral menisci (LM) at baseline, and 83.1% and 88.3% at 12-month follow-up. Medial tibial coverage was significantly lower for arthritic cases compared to non-arthritic ones. Medial meniscal extrusion was significantly higher for arthritic knees. A moderate correlation between automatically computed medial meniscal extrusion and experts' readings was found (ρ=0.44). Mean medial meniscal extrusion was significantly greater for incident OA cases compared to controls (1.16±0.93 mm vs. 0.83±0.92 mm; p&lt;0.05).&#13;
Conclusion: Especially for medial menisci an excellent segmentation accuracy was achieved. Our meniscal biomarkers were validated by comparison to experts' readings as well as analysis of differences w.r.t groups of OA, JSN, and WOMAC pain. It was confirmed that medial meniscal extrusion is a predictor for incident OA.</abstract>
    <parentTitle language="eng">Osteoarthritis and Cartilage</parentTitle>
    <identifier type="doi">10.1016/j.joca.2018.02.907</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-68038</enrichment>
    <enrichment key="AcceptedDate">2018-27-02</enrichment>
    <author>Alexander Tack</author>
    <submitter>Alexander Tack</submitter>
    <author>Anirban Mukhopadhyay</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>6748</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>researchdata</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Knee Menisci Segmentation using Convolutional Neural Networks: Data from the Osteoarthritis Initiative (Supplementary Material)</title>
    <abstract language="eng">Abstract: Objective: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof. Method: A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain. Correlation between computed meniscal extrusion and MOAKS experts' readings was evaluated for 600 subjects. Suitability of biomarkers for predicting incident radiographic OA from baseline to 24 months was tested on a group of 552 patients (184 incident OA, 386 controls) by performing conditional logistic regression. Results: Segmentation accuracy measured as Dice Similarity Coefficient was 83.8% for medial menisci (MM) and 88.9% for lateral menisci (LM) at baseline, and 83.1% and 88.3% at 12-month follow-up. Medial tibial coverage was significantly lower for arthritic cases compared to non-arthritic ones. Medial meniscal extrusion was significantly higher for arthritic knees. A moderate correlation between automatically computed medial meniscal extrusion and experts' readings was found (ρ=0.44). Mean medial meniscal extrusion was significantly greater for incident OA cases compared to controls (1.16±0.93 mm vs. 0.83±0.92 mm; p&lt;0.05). Conclusion: Especially for medial menisci an excellent segmentation accuracy was achieved. Our meniscal biomarkers were validated by comparison to experts' readings as well as analysis of differences w.r.t groups of OA, JSN, and WOMAC pain. It was confirmed that medial meniscal extrusion is a predictor for incident OA.</abstract>
    <identifier type="doi">10.12752/4.TMZ.1.0</identifier>
    <note>Supplementary data to reproduce results from the related publication, including convolutional neural networks' weights.</note>
    <enrichment key="ScientificResourceTypeGeneral">Software</enrichment>
    <enrichment key="ScientificGeolocation">Berlin, Germany</enrichment>
    <enrichment key="ScientificDateCreated">2018</enrichment>
    <enrichment key="SoftwareDescription">The convolutional neural networks' weights were generated using Keras with the backend Theano. The networks were trained on segmentation masks of medial and lateral menisci provided by iMorphics (Manchester, UK). See https://github.com/AlexanderTack/Menisci-Segmentation for the python source code.</enrichment>
    <enrichment key="zib_DownloadUrl">http://www.zib.de/ext-data/2018_Tack_OAC-Supplementary-Material.zip</enrichment>
    <enrichment key="zib_relatedIdentifier">https://doi.org/10.12752/4.TMZ.1.0</enrichment>
    <author>Alexander Tack</author>
    <submitter>Alexander Tack</submitter>
    <author>Anirban Mukhopadhyay</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
  </doc>
  <doc>
    <id>6923</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Ultra-short echo-time (UTE) imaging of the knee with curved surface reconstruction-based extraction of the patellar tendon</title>
    <abstract language="eng">Due to very short T2 relaxation times, imaging of tendons is typically performed using ultra-short echo-time (UTE) acquisition techniques. In this work, we combined an echo-train shifted multi-echo 3D UTE imaging sequence with a 3D curved surface reconstruction to virtually extract the patellar tendon from an acquired 3D UTE dataset. Based on the analysis of the acquired multi-echo data, a T2* relaxation time parameter map was calculated and interpolated to the curved surface of the patellar tendon.</abstract>
    <parentTitle language="eng">ISMRM (International Society for Magnetic Resonance in Medicine), 26th Annual Meeting 2018, Paris, France</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2018-02-03</enrichment>
    <author>Martin Krämer</author>
    <submitter>Christoph von Tycowicz</submitter>
    <author>Marta Maggioni</author>
    <author>Christoph von Tycowicz</author>
    <author>Nick Brisson</author>
    <author>Stefan Zachow</author>
    <author>Georg Duda</author>
    <author>Jürgen Reichenbach</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="persons" number="vontycowicz">Tycowicz, Christoph von</collection>
    <collection role="projects" number="DFG_KneeKinematics">DFG_KneeKinematics</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>6866</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <edition/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2018-05-17</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative</title>
    <abstract language="eng">We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging, that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures. The method is evaluated on data of the MICCAI grand challenge "Segmentation of Knee Images 2010".  For the first time an accuracy equivalent to the inter-observer variability of human readers has been achieved in this challenge. Moreover, the quality of the proposed method is thoroughly assessed using various measures for 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage.  Our method yields sub-voxel accuracy. In conclusion, combining of anatomical knowledge using SSMs with localized classification via CNNs results in a state-of-the-art segmentation method.</abstract>
    <parentTitle language="eng">Medical Imaging with Deep Learning</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="FulltextUrl">https://openreview.net/pdf?id=SJ_-Nx3jz</enrichment>
    <author>Felix Ambellan</author>
    <submitter>Alexander Tack</submitter>
    <author>Alexander Tack</author>
    <author>Moritz Ehlke</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="ehlke">Ehlke, Moritz</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="BMBF-TOKMIS">BMBF-TOKMIS</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>7747</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>10</issue>
    <volume>3755</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-02-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Characterization of the Airflow within an Average Geometry of the Healthy Human Nasal Cavity</title>
    <abstract language="eng">This study’s objective was the generation of a standardized geometry of the healthy nasal cavity.&#13;
An average geometry of the healthy nasal cavity was generated using a statistical shape model based on 25 symptom-free subjects. Airflow within the average geometry and these geometries was calculated using fluid simulations. Integral measures of the nasal resistance, wall shear stresses (WSS) and velocities were calculated as well as cross-sectional areas (CSA). Furthermore, individual WSS and static pressure distributions were mapped onto the average geometry.&#13;
The average geometry featured an overall more regular shape that resulted in less resistance, reduced wall shear stresses and velocities compared to the median of the 25 geometries. Spatial distributions of WSS and pressure of average geometry agreed well compared to the average distributions of all individual geometries. The minimal CSA of the average geometry was larger than the median of all individual geometries (83.4 vs. 74.7 mm²).&#13;
The airflow observed within the average geometry of the healthy nasal cavity did not equal the average airflow of the individual geometries. While differences observed for integral measures were notable, the calculated values for the average geometry lay within the distributions of the individual parameters. Spatially resolved parameters differed less prominently.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="doi">10.1038/s41598-020-60755-3</identifier>
    <identifier type="url">https://rdcu.be/b2irD</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">17.02.2020</enrichment>
    <author>Jan Brüning</author>
    <submitter>Stefan Zachow</submitter>
    <author>Thomas Hildebrandt</author>
    <author>Werner Heppt</author>
    <author>Nora Schmidt</author>
    <author>Hans Lamecker</author>
    <author>Angelika Szengel</author>
    <author>Natalja Amiridze</author>
    <author>Heiko Ramm</author>
    <author>Matthias Bindernagel</author>
    <author>Stefan Zachow</author>
    <author>Leonid Goubergrits</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="lamecker">Lamecker, Hans</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="Rhino-CFD">Rhino-CFD</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>7982</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>16</pageFirst>
    <pageLast>27</pageLast>
    <pageNumber/>
    <edition>1</edition>
    <issue/>
    <volume>12439</volume>
    <type>article</type>
    <publisherName>Springer International Publishing</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-12-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks</title>
    <abstract language="eng">We present an automated method for extrapolating missing&#13;
regions in label data of the skull in an anatomically plausible manner. The ultimate goal is to design patient-speci� c cranial implants for correcting large, arbitrarily shaped defects of the skull that can, for example, result from trauma of the head. Our approach utilizes a 3D statistical shape model (SSM) of the skull and a 2D generative adversarial network (GAN) that is trained in an unsupervised fashion from samples of healthy patients alone. By � tting the SSM to given input labels containing the skull defect, a First approximation of the healthy state of the patient is obtained. The GAN is then applied to further correct and smooth the output of the SSM in an anatomically plausible manner. Finally, the defect region is extracted using morphological operations and subtraction between the extrapolated healthy state of the patient and the defective input labels. The method is trained and evaluated based on data from the MICCAI 2020 AutoImplant challenge. It produces state-of-the art results on regularly&#13;
shaped cut-outs that were present in the training and testing data of the challenge. Furthermore, due to unsupervised nature of the approach, the method generalizes well to previously unseen defects of varying shapes that were only present in the hidden test dataset.</abstract>
    <parentTitle language="eng">Towards the Automatization of Cranial Implant Design in Cranioplasty</parentTitle>
    <subTitle language="eng">First Challenge, AutoImplant 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings</subTitle>
    <identifier type="doi">10.1007/978-3-030-64327-0_3</identifier>
    <note>Best Paper Award</note>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2020-09-21</enrichment>
    <author>Pedro Pimentel</author>
    <submitter>Stefan Zachow</submitter>
    <editor>Jianning Li</editor>
    <author>Angelika Szengel</author>
    <editor>Jan Egger</editor>
    <author>Moritz Ehlke</author>
    <author>Hans Lamecker</author>
    <author>Stefan Zachow</author>
    <author>Laura Estacio</author>
    <author>Christian Doenitz</author>
    <author>Heiko Ramm</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="ehlke">Ehlke, Moritz</collection>
    <collection role="persons" number="lamecker">Lamecker, Hans</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="Cranio">Cranio</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>6769</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2018-03-07</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Spotting the Details: The Various Facets of Facial Expressions</title>
    <abstract language="eng">3D Morphable Models (MM) are a popular tool for analysis and synthesis of facial expressions. They represent plausible variations in facial shape and appearance within a low-dimensional parameter space. Fitted to a face scan, the model's parameters compactly encode its expression patterns. This expression code can be used, for instance, as a feature in automatic facial expression recognition. For accurate classification, an MM that can adequately represent the various characteristic facets and variants of each expression is necessary. Currently available MMs are limited in the diversity of expression patterns. We present a novel high-quality Facial Expression Morphable Model built from a large-scale face database as a tool for expression analysis and synthesis. Establishment of accurate dense correspondence, up to finest skin features, enables a detailed statistical analysis of facial expressions. Various characteristic shape patterns are identified for each expression. The results of our analysis give rise to a new facial expression code. We demonstrate the advantages of such a code for the automatic recognition of expressions, and compare the accuracy of our classifier to state-of-the-art.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-67696</identifier>
    <author>Carl Martin Grewe</author>
    <submitter>Carl Martin Grewe</submitter>
    <author>Gabriel Le Roux</author>
    <author>Sven-Kristofer Pilz</author>
    <author>Stefan Zachow</author>
    <series>
      <title>ZIB-Report</title>
      <number>18-06</number>
    </series>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="IKG-FacialMorphology">IKG-FacialMorphology</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/6769/zibreport.pdf</file>
  </doc>
  <doc>
    <id>6781</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>87</pageFirst>
    <pageLast>125</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>38</volume>
    <type>article</type>
    <publisherName>Wiley</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-05-07</completedDate>
    <publishedDate>2019-02-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Generation and Visual Exploration of Medical Flow Data: Survey, Research Trends, and Future Challenges</title>
    <abstract language="eng">Simulations and measurements of blood and air flow inside the human circulatory and respiratory system play an increasingly important role in personalized medicine for prevention, diagnosis, and treatment of diseases. This survey focuses on three main application areas. (1) Computational Fluid Dynamics (CFD) simulations of blood flow in cerebral aneurysms assist in predicting the outcome of this pathologic process and of therapeutic interventions. (2) CFD simulations of nasal airflow allow for investigating the effects of obstructions and deformities and provide therapy decision support. (3) 4D Phase-Contrast (4D PC) Magnetic Resonance Imaging (MRI) of aortic hemodynamics supports the diagnosis of various vascular and valve pathologies as well as their treatment. An investigation of the complex and often dynamic simulation and measurement data requires the coupling of sophisticated visualization, interaction, and data analysis techniques.&#13;
&#13;
In this paper, we survey the large body of work that has been conducted within this realm. We extend previous surveys by incorporating nasal airflow, addressing the joint investigation of blood flow and vessel wall properties, and providing a more fine-granular taxonomy of the existing techniques. From the survey, we extract major research trends and identify open problems and future challenges. The survey is intended for researchers interested in medical flow but also more general, in the combined visualization of physiology and anatomy, the extraction of features from flow field data and feature-based visualization, the visual comparison of different simulation results, and the interactive visual analysis of the flow field and derived characteristics.</abstract>
    <parentTitle language="eng">Computer Graphics Forum</parentTitle>
    <identifier type="doi">10.1111/cgf.13394</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2018-05-07</enrichment>
    <author>Steffen Oeltze-Jaffra</author>
    <submitter>Stefan Zachow</submitter>
    <author>Monique Meuschke</author>
    <author>Mathias Neugebauer</author>
    <author>Sylvia Saalfeld</author>
    <author>Kai Lawonn</author>
    <author>Gabor Janiga</author>
    <author>Hans-Christian Hege</author>
    <author>Stefan Zachow</author>
    <author>Bernhard Preim</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="visalgo">Visual Data Analysis in Science and Engineering</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="FLOW-VIS">FLOW-VIS</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Rhino-CFD">Rhino-CFD</collection>
    <collection role="projects" number="FLOW-ANALYSISII">FLOW-ANALYSISII</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>6803</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>680</pageFirst>
    <pageLast>688</pageLast>
    <pageNumber/>
    <edition/>
    <issue>5</issue>
    <volume>26</volume>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-03-14</completedDate>
    <publishedDate>2018-03-14</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Knee Menisci Segmentation using Convolutional Neural Networks: Data from the Osteoarthritis Initiative</title>
    <abstract language="eng">Abstract: Objective: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof. Method: A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain. Correlation between computed meniscal extrusion and MOAKS experts' readings was evaluated for 600 subjects. Suitability of biomarkers for predicting incident radiographic OA from baseline to 24 months was tested on a group of 552 patients (184 incident OA, 386 controls) by performing conditional logistic regression. Results: Segmentation accuracy measured as Dice Similarity Coefficient was 83.8% for medial menisci (MM) and 88.9% for lateral menisci (LM) at baseline, and 83.1% and 88.3% at 12-month follow-up. Medial tibial coverage was significantly lower for arthritic cases compared to non-arthritic ones. Medial meniscal extrusion was significantly higher for arthritic knees. A moderate correlation between automatically computed medial meniscal extrusion and experts' readings was found (ρ=0.44). Mean medial meniscal extrusion was significantly greater for incident OA cases compared to controls (1.16±0.93 mm vs. 0.83±0.92 mm; p&lt;0.05). Conclusion: Especially for medial menisci an excellent segmentation accuracy was achieved. Our meniscal biomarkers were validated by comparison to experts' readings as well as analysis of differences w.r.t groups of OA, JSN, and WOMAC pain. It was confirmed that medial meniscal extrusion is a predictor for incident OA.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-68038</identifier>
    <author>Alexander Tack</author>
    <submitter>Alexander Tack</submitter>
    <author>Anirban Mukhopadhyay</author>
    <author>Stefan Zachow</author>
    <series>
      <title>ZIB-Report</title>
      <number>18-15</number>
    </series>
    <collection role="ccs" number="">Health</collection>
    <collection role="msc" number="68T99">None of the above, but in this section</collection>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/6803/Tack_OAC_ZIB_Report.pdf</file>
  </doc>
  <doc>
    <id>6669</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted>2018</thesisYearAccepted>
    <language>eng</language>
    <pageFirst>2815</pageFirst>
    <pageLast>2826</pageLast>
    <pageNumber>12</pageNumber>
    <edition/>
    <issue>9</issue>
    <volume>54</volume>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-03-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Uncertainty in Temperature-Based Determination of Time of Death</title>
    <abstract language="eng">Temperature-based estimation of time of death (ToD) can be per-&#13;
formed either with the help of simple phenomenological models of corpse&#13;
cooling or with detailed mechanistic (thermodynamic) heat transfer mod-&#13;
els. The latter are much more complex, but allow a higher accuracy of&#13;
ToD estimation as in principle all relevant cooling mechanisms can be&#13;
taken into account.&#13;
The potentially higher accuracy depends on the accuracy of tissue and&#13;
environmental parameters as well as on the geometric resolution. We in-&#13;
vestigate the impact of parameter variations and geometry representation&#13;
on the estimated ToD based on a highly detailed 3D corpse model, that&#13;
has been segmented and geometrically reconstructed from a computed to-&#13;
mography (CT) data set, differentiating various organs and tissue types.</abstract>
    <parentTitle language="eng">Heat and Mass Transfer</parentTitle>
    <identifier type="doi">10.1007/s00231-018-2324-4</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-63818</enrichment>
    <enrichment key="AcceptedDate">2018-03-12</enrichment>
    <author>Martin Weiser</author>
    <submitter>Martin Weiser</submitter>
    <author>Bodo Erdmann</author>
    <author>Sebastian Schenkl</author>
    <author>Holger Muggenthaler</author>
    <author>Michael Hubig</author>
    <author>Gita Mall</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="compmed">Computational Medicine</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="weiser">Weiser, Martin</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="UJena-Forensic">UJena-Forensic</collection>
    <collection role="projects" number="ZIB-Kaskade7">ZIB-Kaskade7</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>7434</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>182</pageFirst>
    <pageLast>188</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Localization and Classification of Teeth in Cone Beam CT using Convolutional Neural Networks</title>
    <abstract language="eng">In dentistry, software-based medical image analysis and visualization provide efficient and accurate diagnostic and therapy planning capabilities. We present an approach for the automatic recognition of tooth types and positions in digital volume tomography (DVT). By using deep learning techniques in combination with dimensionality reduction through non-planar reformatting of the jaw anatomy, DVT data can be efficiently processed and&#13;
teeth reliably recognized and classified, even in the presence of imaging artefacts, missing or dislocated teeth.&#13;
We evaluated our approach, which is based on 2D Convolutional Neural Networks (CNNs), on 118 manually&#13;
annotated cases of clinical DVT datasets. Our proposed method correctly classifies teeth with an accuracy of&#13;
94% within a limit of 2mm distance to ground truth labels.</abstract>
    <parentTitle language="eng">Proc. of the  18th annual conference on Computer- and Robot-assisted Surgery (CURAC)</parentTitle>
    <identifier type="isbn">978-3-00-063717-9</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">02.08.2019</enrichment>
    <author>Mario Neumann</author>
    <submitter>Stefan Zachow</submitter>
    <author>Olaf Hellwich</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="Dental">Dental</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>7436</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>58</pageFirst>
    <pageLast>64</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Collision-Aware Articulated Statistical Shape Model of the Human Spine</title>
    <abstract language="eng">Statistical Shape Models (SSMs) are a proven means for model-based 3D anatomy reconstruction from medical&#13;
image data. In orthopaedics and biomechanics, SSMs are increasingly employed to individualize measurement&#13;
data or to create individualized anatomical models to which implants can be adapted to or functional tests can be performed on. For modeling and analysis of articulated structures, so called articulated SSMs (aSSMs) have been developed. However, a missing feature of aSSMs is the consideration of collisions in the course of individual fitting and articulation. The aim of our work was to develop aSSMs that handle collisions between components correctly. That way it becomes possible to adjust shape and articulation in view of a physically and geometrically plausible individualization. To be able to apply collision-aware aSSMs in simulation and optimisation, our approach is based on an e� cient collision detection method employing Graphics Processing Units (GPUs).</abstract>
    <parentTitle language="eng">Proc. of the  18th annual conference on Computer- and Robot-assisted Surgery (CURAC)</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">02.08.2019</enrichment>
    <author>Robert Joachimsky</author>
    <submitter>Stefan Zachow</submitter>
    <author>Lihong Ma</author>
    <author>Christian Icking</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="Spine">Spine</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>7437</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>29</pageFirst>
    <pageLast>36</pageLast>
    <pageNumber/>
    <edition/>
    <issue>11</issue>
    <volume>63</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">T1 and T2* mapping of the human quadriceps and patellar tendons using ultra-short echo-time (UTE) imaging and bivariate relaxation parameter-based volumetric visualization</title>
    <abstract language="eng">Quantification of magnetic resonance (MR)-based relaxation parameters of tendons and ligaments is challenging due to their very short transverse relaxation times, requiring application of ultra-short echo-time (UTE) imaging sequences. We quantify both T1 and T2⁎ in the quadriceps and patellar tendons of healthy volunteers at a field strength of 3 T and visualize the results based on 3D segmentation by using bivariate histogram analysis. We applied a 3D ultra-short echo-time imaging sequence with either variable repetition times (VTR) or variable flip angles (VFA) for T1 quantification in combination with multi-echo acquisition for extracting T2⁎. The values of both relaxation parameters were subsequently binned for bivariate histogram analysis and corresponding cluster identification, which were subsequently visualized. Based on manually-drawn regions of interest in the tendons on the relaxation parameter maps, T1 and T2⁎ boundaries were selected in the bivariate histogram to segment the quadriceps and patellar tendons and visualize the relaxation times by 3D volumetric rendering. Segmentation of bone marrow, fat, muscle and tendons was successfully performed based on the bivariate histogram analysis. Based on the segmentation results mean T2⁎ relaxation times, over the entire tendon volumes averaged over all subjects, were 1.8 ms ± 0.1 ms and 1.4 ms ± 0.2 ms for the patellar and quadriceps tendons, respectively. The mean T1 value of the patellar tendon, averaged over all subjects, was 527 ms ± 42 ms and 476 ms ± 40 ms for the VFA and VTR acquisitions, respectively. The quadriceps tendon had higher mean T1 values of 662 ms ± 97 ms (VFA method) and 637 ms ± 40 ms (VTR method) compared to the patellar tendon. 3D volumetric visualization of the relaxation times revealed that T1 values are not constant over the volume of both tendons, but vary locally. This work provided additional data to build upon the scarce literature available on relaxation times in the quadriceps and patellar tendons. We were able to segment both tendons and to visualize the relaxation parameter distributions over the entire tendon volumes.</abstract>
    <parentTitle language="eng">Magnetic Resonance Imaging</parentTitle>
    <identifier type="doi">10.1016/j.mri.2019.07.015</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Martin Krämer</author>
    <submitter>Stefan Zachow</submitter>
    <author>Marta Maggioni</author>
    <author>Nicholas Brisson</author>
    <author>Stefan Zachow</author>
    <author>Ulf Teichgräber</author>
    <author>Georg Duda</author>
    <author>Jürgen Reichenbach</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="DFG_KneeKinematics">DFG_KneeKinematics</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>7456</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2019-08-28</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">An as-invariant-as-possible GL+(3)-based Statistical Shape Model</title>
    <abstract language="eng">We describe a novel nonlinear statistical shape model basedon differential coordinates viewed as elements of GL+(3). We adopt an as-invariant-as possible framework comprising a bi-invariant Lie group mean and a tangent principal component analysis based on a unique GL+(3)-left-invariant, O(3)-right-invariant metric. Contrary to earlier work that equips the coordinates with a specifically constructed group structure, our method employs the inherent geometric structure of the group-valued data and therefore features an improved statistical power in identifying shape differences. We demonstrate this in experiments on two anatomical datasets including comparison to the standard Euclidean as well as recent state-of-the-art nonlinear approaches to statistical shape modeling.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-74566</identifier>
    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Stefan Zachow</author>
    <author>Christoph von Tycowicz</author>
    <series>
      <title>ZIB-Report</title>
      <number>19-46</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Statistical shape analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Tangent principal component analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Lie groups</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Manifold valued statistics</value>
    </subject>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="persons" number="vontycowicz">Tycowicz, Christoph von</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="MathPlus-TrU-1">MathPlus-TrU-1</collection>
    <collection role="projects" number="MathPlus-EF2-3">MathPlus-EF2-3</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/7456/ZIBReport_19-46.pdf</file>
  </doc>
  <doc>
    <id>7448</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>219</pageFirst>
    <pageLast>228</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>11846</volume>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">An as-invariant-as-possible GL+(3)-based Statistical Shape Model</title>
    <abstract language="eng">We describe a novel nonlinear statistical shape model basedon differential coordinates viewed as elements of GL+(3). We adopt an as-invariant-as possible framework comprising a bi-invariant Lie group mean and a tangent principal component analysis based on a unique GL+(3)-left-invariant, O(3)-right-invariant metric. Contrary to earlier work that equips the coordinates with a specifically constructed group structure, our method employs the inherent geometric structure of the group-valued data and therefore features an improved statistical power in identifying shape differences. We demonstrate this in experiments on two anatomical datasets including comparison to the standard Euclidean as well as recent state-of-the-art nonlinear approaches to statistical shape modeling.</abstract>
    <parentTitle language="eng">Proc. 7th MICCAI workshop on Mathematical Foundations of Computational Anatomy (MFCA)</parentTitle>
    <identifier type="doi">10.1007/978-3-030-33226-6_23</identifier>
    <enrichment key="Series">Lecture Notes in Computer Science</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2019-08-19</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-74566</enrichment>
    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Stefan Zachow</author>
    <author>Christoph von Tycowicz</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="persons" number="vontycowicz">Tycowicz, Christoph von</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="MathPlus-TrU-1">MathPlus-TrU-1</collection>
    <collection role="projects" number="MathPlus-EF2-3">MathPlus-EF2-3</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="institutes" number="GDAP">Geometric Data Analysis and Processing</collection>
  </doc>
  <doc>
    <id>7449</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2019-08-27</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Surface-Theoretic Approach for Statistical Shape Modeling</title>
    <abstract language="eng">We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. We evaluate the performance of our model w.r.t. shape-based classification of pathological malformations of the human knee and show that it outperforms the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model's ability of capturing natural biological shape variability, we carry out an analysis of specificity and generalization ability.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-74497</identifier>
    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Stefan Zachow</author>
    <author>Christoph von Tycowicz</author>
    <series>
      <title>ZIB-Report</title>
      <number>19-20</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Statistical shape analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Principal geodesic analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Lie groups</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Manifold valued statistics</value>
    </subject>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="persons" number="vontycowicz">Tycowicz, Christoph von</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="MathPlus-TrU-1">MathPlus-TrU-1</collection>
    <collection role="projects" number="MathPlus-EF2-3">MathPlus-EF2-3</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/7449/ZIBReport_19-20.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/7449/ZIB-Report_19-20_supplement.zip</file>
  </doc>
  <doc>
    <id>7342</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>21</pageFirst>
    <pageLast>29</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>11767</volume>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Surface-Theoretic Approach for Statistical Shape Modeling</title>
    <abstract language="eng">We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. We evaluate the performance of our model w.r.t. shape-based classification of pathological malformations of the human knee and show that it outperforms the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model’s ability of capturing natural biological shape variability, we carry out an analysis of specificity and generalization ability.</abstract>
    <parentTitle language="eng">Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), Part IV</parentTitle>
    <identifier type="doi">10.1007/978-3-030-32251-9_3</identifier>
    <enrichment key="Series">Lecture Notes in Computer Science</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2019-06-05</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-74497</enrichment>
    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Stefan Zachow</author>
    <author>Christoph von Tycowicz</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="persons" number="vontycowicz">Tycowicz, Christoph von</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="MathPlus-TrU-1">MathPlus-TrU-1</collection>
    <collection role="projects" number="MathPlus-EF2-3">MathPlus-EF2-3</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="institutes" number="GDAP">Geometric Data Analysis and Processing</collection>
  </doc>
  <doc>
    <id>8123</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>849</pageFirst>
    <pageLast>859</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>16</volume>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-05-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentation</title>
    <abstract language="eng">Purpose&#13;
&#13;
Segmentation of surgical instruments in endoscopic video streams is essential for automated surgical scene understanding and process modeling. However, relying on fully supervised deep learning for this task is challenging because manual annotation occupies valuable time of the clinical experts.&#13;
&#13;
Methods&#13;
&#13;
We introduce a teacher–student learning approach that learns jointly from annotated simulation data and unlabeled real data to tackle the challenges in simulation-to-real unsupervised domain adaptation for endoscopic image segmentation.&#13;
&#13;
Results&#13;
&#13;
Empirical results on three datasets highlight the effectiveness of the proposed framework over current approaches for the endoscopic instrument segmentation task. Additionally, we provide analysis of major factors affecting the performance on all datasets to highlight the strengths and failure modes of our approach.&#13;
&#13;
Conclusions&#13;
&#13;
We show that our proposed approach can successfully exploit the unlabeled real endoscopic video frames and improve generalization performance over pure simulation-based training and the previous state-of-the-art. This takes us one step closer to effective segmentation of surgical instrument in the annotation scarce setting.</abstract>
    <parentTitle language="eng">International Journal of Computer Assisted Radiology and Surgery</parentTitle>
    <identifier type="doi">10.1007/s11548-021-02383-4</identifier>
    <identifier type="arxiv">arXiv:2103.01593</identifier>
    <note>Honorary Mention: Machine Learning for Computer-Assisted Intervention (CAI) Award @IPCAI2021</note>
    <note>Honorary Mention: Audience Award for Best Innovation @IPCAI2021</note>
    <enrichment key="PeerReviewed">yes</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Manish Sahu</author>
    <submitter>Manish Sahu</submitter>
    <author>Anirban Mukhopadhyay</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="medplan">Therapy Planning</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="projects" number="BMBF-COMPASS">BMBF-COMPASS</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/8123/pre-print.pdf</file>
  </doc>
  <doc>
    <id>8090</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images</title>
    <parentTitle language="eng">arXiv</parentTitle>
    <identifier type="arxiv">2001.09193</identifier>
    <enrichment key="PeerReviewed">no</enrichment>
    <enrichment key="FulltextUrl">https://arxiv.org/pdf/2001.09193.pdf</enrichment>
    <submitter>Tamaz Amiranashvili</submitter>
    <author>Anjany Sekuboyina</author>
    <author>Amirhossein Bayat</author>
    <author>Malek E. Husseini</author>
    <author>Maximilian Löffler</author>
    <author>Hongwei Li</author>
    <author>Giles Tetteh</author>
    <author>Jan Kukačka</author>
    <author>Christian Payer</author>
    <author>Darko Štern</author>
    <author>Martin Urschler</author>
    <author>Maodong Chen</author>
    <author>Dalong Cheng</author>
    <author>Nikolas Lessmann</author>
    <author>Yujin Hu</author>
    <author>Tianfu Wang</author>
    <author>Dong Yang</author>
    <author>Daguang Xu</author>
    <author>Felix Ambellan</author>
    <author>Tamaz Amiranashvili</author>
    <author>Moritz Ehlke</author>
    <author>Hans Lamecker</author>
    <author>Sebastian Lehnert</author>
    <author>Marilia Lirio</author>
    <author>Nicolás Pérez de Olaguer</author>
    <author>Heiko Ramm</author>
    <author>Manish Sahu</author>
    <author>Alexander Tack</author>
    <author>Stefan Zachow</author>
    <author>Tao Jiang</author>
    <author>Xinjun Ma</author>
    <author>Christoph Angerman</author>
    <author>Xin Wang</author>
    <author>Qingyue Wei</author>
    <author>Kevin Brown</author>
    <author>Matthias Wolf</author>
    <author>Alexandre Kirszenberg</author>
    <author>Élodie Puybareau</author>
    <author>Alexander Valentinitsch</author>
    <author>Markus Rempfler</author>
    <author>Björn H. Menze</author>
    <author>Jan S. Kirschke</author>
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    <parentTitle language="eng">Proc. BMT, Biomed Tech</parentTitle>
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    <author>Hans Lamecker</author>
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    <author>Jens von Berg</author>
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    <author>Max Kahnt</author>
    <author>Francis Galloway</author>
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    <author>Sharma GB</author>
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