<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>6068</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1602.08425v1</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">Shape-aware Surface Reconstruction from Sparse Data</title>
    <abstract language="eng">The  reconstruction  of  an  object's  shape  or  surface  from  a  set  of  3D  points  is  a  common  topic  in  materials  and  life sciences, computationally handled in computer graphics.  Such points usually stem from optical or tactile 3D coordinate measuring equipment.  Surface reconstruction also appears in medical image analysis, e.g.  in anatomy reconstruction from  tomographic  measurements  or  the  alignment  of  intra-operative  navigation  and  preoperative  planning  data.   In contrast to mere 3D point clouds, medical imaging yields contextual information on the 3D point data that can be used to adopt prior information on the shape that is to be reconstructed from the measurements.  In this work we propose to use a statistical shape model (SSM) as a prior for surface reconstruction.  The prior knowledge is represented by a point distribution model (PDM) that is associated with a surface mesh.  Using the shape distribution that is modelled by the PDM, we reformulate the problem of surface reconstruction from a probabilistic perspective based on a Gaussian Mixture Model (GMM). In order to do so, the given measurements 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  tting is accomplished.  By estimating the parameters of the GMM in a maximum a posteriori manner, the  reconstruction  of  the  surface  from  the  given  measurements  is  achieved.   Extensive  experiments  suggest  that  our proposed approach leads to superior surface reconstructions compared to Iterative Closest Point (ICP) methods.</abstract>
    <parentTitle language="eng">arXiv</parentTitle>
    <identifier type="arxiv">arXiv:1602.08425v1</identifier>
    <enrichment key="PeerReviewed">no</enrichment>
    <enrichment key="FulltextUrl">https://arxiv.org/pdf/1602.08425v1.pdf</enrichment>
    <author>Florian Bernard</author>
    <submitter>Florian Bernard</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="persons" number="lamecker">Lamecker, Hans</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>
  </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>2180</id>
    <completedYear>2002</completedYear>
    <publishedYear>2002</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>61</pageFirst>
    <pageLast>66</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>12</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A nonlinear elastic soft tissue model for craniofacial surgery simulations</title>
    <parentTitle language="eng">ESAIM, Proc.</parentTitle>
    <identifier type="doi">10.1051/proc:2002011</identifier>
    <author>Evgeny Gladilin</author>
    <author>Stefan Zachow</author>
    <author>Peter Deuflhard</author>
    <author>Hans-Christian Hege</author>
    <collection role="institutes" number="num">Numerical Mathematics</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>
  </doc>
  <doc>
    <id>2181</id>
    <completedYear>2002</completedYear>
    <publishedYear>2002</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>INRIA</publisherName>
    <publisherPlace>Paris, France</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A nonlinear soft tissue model for craniofacial surgery simulations</title>
    <parentTitle language="eng">Proc. of Modeling and Simulation for Computer-aided Medicine and Surgery (MS4CMS</parentTitle>
    <author>Evgeny Gladilin</author>
    <author>Stefan Zachow</author>
    <author>Peter Deuflhard</author>
    <author>Hans-Christian Hege</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="institutes" number="compmed">Computational Medicine</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>
  </doc>
  <doc>
    <id>2182</id>
    <completedYear>2002</completedYear>
    <publishedYear>2002</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>7</pageFirst>
    <pageLast>11</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace>Malaga, Spain</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Biomechanical modeling of individual facial emotion expressions</title>
    <parentTitle language="eng">Proc. of Visualization, Imaging, and Image Processing (VIIP)</parentTitle>
    <author>Evgeny Gladilin</author>
    <author>Stefan Zachow</author>
    <author>Peter Deuflhard</author>
    <author>Hans-Christian Hege</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="institutes" number="compmed">Computational Medicine</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>
  </doc>
  <doc>
    <id>2183</id>
    <completedYear>2002</completedYear>
    <publishedYear>2002</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>25</pageFirst>
    <pageLast>28</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace>Leipzig, Germany</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Biomechanisches Modell zur Abschätzung der individuellen Gesichtsmimik</title>
    <parentTitle language="eng">Proc.of Workshop Bildverarbeitung für die Medizin (BVM)</parentTitle>
    <author>Evgeny Gladilin</author>
    <editor>M. Meiler</editor>
    <author>Stefan Zachow</author>
    <editor>D. Saupe</editor>
    <author>Peter Deuflhard</author>
    <editor>F. Krugel</editor>
    <author>Hans-Christian Hege</author>
    <editor>H. Handels</editor>
    <editor>T. Lehmann</editor>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="institutes" number="compmed">Computational Medicine</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>
  </doc>
  <doc>
    <id>2590</id>
    <completedYear>2005</completedYear>
    <publishedYear>2005</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>277</pageFirst>
    <pageLast>298</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>bookpart</type>
    <publisherName>Research Signpost</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Soft Tissue Prediction in Computer Assisted Maxillofacial Surgery Planning</title>
    <parentTitle language="eng">Biomechanics Applied to Computer Assisted Surgery</parentTitle>
    <enrichment key="FulltextUrl">http://www.zib.de/zachow/publications/zachow-rsp-2005.pdf</enrichment>
    <author>Stefan Zachow</author>
    <editor>Y. Payan</editor>
    <author>Martin Weiser</author>
    <author>Hans-Christian Hege</author>
    <author>Peter Deuflhard</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="institutes" number="compmed">Computational Medicine</collection>
    <collection role="persons" number="deuflhard">Deuflhard, Peter</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</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>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>2424</id>
    <completedYear>2011</completedYear>
    <publishedYear>2011</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>567</pageFirst>
    <pageLast>571</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>29</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The medial-lateral force distribution in the ovine stifle joint during walking</title>
    <parentTitle language="eng">Journal of Orthopaedic Research</parentTitle>
    <identifier type="doi">10.1002/jor.21254</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>William R. Taylor</author>
    <author>Berry M. Pöpplau</author>
    <author>Christian König</author>
    <author>Rainald Ehrig</author>
    <author>Stefan Zachow</author>
    <author>Georg Duda</author>
    <author>Markus O. Heller</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="institutes" number="compmed">Computational Medicine</collection>
    <collection role="persons" number="ehrig">Ehrig, Rainald</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="OSSCA">OSSCA</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>6545</id>
    <completedYear>2017</completedYear>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>67</pageFirst>
    <pageLast>79</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>232</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2017-11-23</completedDate>
    <publishedDate>2018-01-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Changes in Knee Shape and Geometry Resulting from Total Knee Arthroplasty</title>
    <abstract language="eng">Changes in knee shape and geometry resulting from total knee arthroplasty can affect patients in numerous important ways: pain, function, stability, range of motion, and kinematics. Quantitative data concerning these changes have not been previously available, to our knowledge, yet are essential to understand individual experiences of total knee arthroplasty and thereby improve outcomes for all patients. The limiting factor has been the challenge of accurately measuring these changes. Our study objective was to develop a conceptual framework and analysis method to investigate changes in knee shape and geometry, and prospectively apply it to a sample total knee arthroplasty population. Using clinically available computed tomography and radiography imaging systems, the three-dimensional knee shape and geometry of nine patients (eight varus and one valgus) were compared before and after total knee arthroplasty. All patients had largely good outcomes after their total knee arthroplasty. Knee shape changed both visually and numerically. On average, the distal condyles were slightly higher medially and lower laterally (range: +4.5 mm to −4.4 mm), the posterior condyles extended farther out medially but not laterally (range: +1.8 to −6.4 mm), patellofemoral distance increased throughout flexion by 1.8–3.5 mm, and patellar thickness alone increased by 2.9 mm (range: 0.7–5.2 mm). External femoral rotation differed preop and postop. Joint line distance, taking cartilage into account, changed by +0.7 to −1.5 mm on average throughout flexion. Important differences in shape and geometry were seen between pre-total knee arthroplasty and post-total knee arthroplasty knees. While this is qualitatively known, this is the first study to report it quantitatively, an important precursor to identifying the reasons for the poor outcome of some patients. Using the developed protocol and visualization techniques to compare patients with good versus poor clinical outcomes could lead to changes in implant design, implant selection, component positioning, and surgical technique. Recommendations based on this sample population are provided. Intraoperative and postoperative feedback could ultimately improve patient satisfaction.</abstract>
    <parentTitle language="eng">Journal of Engineering in Medicine</parentTitle>
    <identifier type="url">http://journals.sagepub.com/eprint/ZVgNrNESA9EjIcaFWSjb/full</identifier>
    <identifier type="doi">10.1177/0954411917743274</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Mohsen Akbari Shandiz</author>
    <submitter>Stefan Zachow</submitter>
    <author>Paul Boulos</author>
    <author>Stefan Sævarsson</author>
    <author>Heiko Ramm</author>
    <author>Chun Kit Fu</author>
    <author>Stephen Miller</author>
    <author>Stefan Zachow</author>
    <author>Carolyn Anglin</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-TOKMIS">BMBF-TOKMIS</collection>
    <collection role="projects" number="JointKinematics">JointKinematics</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>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/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <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>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <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>7902</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>12263</volume>
    <type>conferenceobject</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-03-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation</title>
    <abstract language="eng">Surgical tool segmentation in endoscopic videos is an important component of computer assisted interventions systems. Recent success of image-based solutions using fully-supervised deep learning approaches can be attributed to the collection of big labeled datasets. However, the annotation of a big dataset of real videos can be prohibitively expensive and time consuming. Computer simulations could alleviate the manual labeling problem, however, models trained on simulated data do not generalize to real data. This work proposes a consistency-based framework for joint learning of simulated and real (unlabeled) endoscopic data to bridge this performance generalization issue. Empirical results on two data sets (15 videos of the Cholec80 and EndoVis'15 dataset) highlight the effectiveness of the proposed Endo-Sim2Real method for instrument segmentation. We compare the segmentation of the proposed approach with state-of-the-art solutions and show that our method improves segmentation both in terms of quality and quantity.</abstract>
    <parentTitle language="eng">Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), Part III</parentTitle>
    <identifier type="doi">https://doi.org/10.1007/978-3-030-59716-0_75</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2020-06-23</enrichment>
    <enrichment key="Series">Lecture Notes in Computer Science</enrichment>
    <enrichment key="PreprintUrn">https://arxiv.org/pdf/2007.11514</enrichment>
    <author>Manish Sahu</author>
    <submitter>Manish Sahu</submitter>
    <author>Ronja Strömsdörfer</author>
    <author>Anirban Mukhopadhyay</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="institutes" number="MfLMS">Mathematics for Life and Materials Science</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="projects" number="BMBF-COMPASS">BMBF-COMPASS</collection>
  </doc>
  <doc>
    <id>7906</id>
    <completedYear>2020</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>20200037</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>6</volume>
    <type>article</type>
    <publisherName>De Gruyter</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-09-17</completedDate>
    <publishedDate>2020-09-17</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Surgical phase recognition by learning phase transitions</title>
    <abstract language="eng">Automatic recognition of surgical phases is an important component for developing an intra-operative context-aware system. Prior work in this area focuses on recognizing short-term tool usage patterns within surgical phases. However, the difference between intra- and inter-phase tool usage patterns has not been investigated for automatic phase recognition. We developed a Recurrent Neural Network (RNN), in particular a state-preserving Long Short Term Memory (LSTM) architecture to utilize the long-term evolution of tool usage within complete surgical procedures. For fully automatic tool presence detection from surgical video frames, a Convolutional Neural Network (CNN) based architecture namely ZIBNet is employed. Our proposed approach outperformed EndoNet by 8.1% on overall precision for phase detection tasks and 12.5% on meanAP for tool recognition tasks.</abstract>
    <parentTitle language="eng">Current Directions in Biomedical Engineering (CDBME)</parentTitle>
    <identifier type="doi">https://doi.org/10.1515/cdbme-2020-0037</identifier>
    <note>Nomination for the Best-Paper Award</note>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2020-06-15</enrichment>
    <author>Manish Sahu</author>
    <submitter>Manish Sahu</submitter>
    <author>Angelika Szengel</author>
    <author>Anirban Mukhopadhyay</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="institutes" number="MfLMS">Mathematics for Life and Materials Science</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="projects" number="BMBF-COMPASS">BMBF-COMPASS</collection>
    <thesisPublisher>Zuse Institute Berlin (ZIB)</thesisPublisher>
  </doc>
  <doc>
    <id>7907</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>other</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Analyzing laparoscopic cholecystectomy with deep learning: automatic detection of surgical tools and phases</title>
    <abstract language="eng">Motivation: The ever-rising volume of patients, high maintenance cost of operating rooms and time consuming analysis of surgical skills are fundamental problems that hamper the practical training of the next generation of surgeons. The hospitals prefer to keep the surgeons busy in real operations over training young surgeons for obvious economic reasons. One fundamental need in surgical training is the reduction of the time needed by the senior surgeon to review the endoscopic procedures performed  by the young surgeon while minimizing the subjective bias in evaluation. The unprecedented performance of deep learning ushers the new age of data-driven automatic analysis of surgical skills. &#13;
&#13;
Method: Deep learning is capable of efficiently analyzing thousands of hours of laparoscopic video footage to provide an objective assessment of surgical skills. However, the traditional end-to-end setting of deep learning (video in, skill assessment out) is not explainable. Our strategy is to utilize the surgical process modeling framework to divide the surgical process into understandable components. This provides the opportunity to employ deep learning for superior yet automatic detection and evaluation of several aspects of laparoscopic cholecystectomy such as surgical tool and phase detection. &#13;
We employ ZIBNet for the detection of surgical tool presence. ZIBNet employs pre-processing based on tool usage imbalance, a transfer learned 50-layer residual network (ResNet-50) and temporal smoothing. To encode the temporal evolution of tool usage (over the entire video sequence) that relates to the surgical phases, Long Short Term Memory (LSTM) units are employed with long-term dependency. &#13;
&#13;
Dataset: We used CHOLEC 80 dataset that consists of 80 videos of laparoscopic cholecystectomy performed by 13 surgeons, divided equally for training and testing. In these videos, up to three different tools (among 7 types of tools) can be present in a frame. &#13;
&#13;
Results: The mean average precision of the detection of all tools is 93.5 ranging between 86.8 and 99.3, a significant improvement (p &lt;0.01) over the previous state-of-the-art. We observed that less frequent tools like Scissors, Irrigator, Specimen Bag etc. are more related to phase transitions. The overall precision (recall) of the detection of all surgical phases is 79.6 (81.3).&#13;
&#13;
Conclusion: While this is not the end goal for surgical skill analysis, the development of such a technological platform is essential toward a data-driven objective understanding of surgical skills. In future, we plan to investigate surgeon-in-the-loop analysis and feedback for surgical skill analysis.</abstract>
    <parentTitle language="eng">28th International Congress of the European Association for Endoscopic Surgery (EAES)</parentTitle>
    <identifier type="url">https://academy.eaes.eu/eaes/2020/28th/298882/manish.sahu.analyzing.laparoscopic.cholecystectomy.with.deep.learning.html?f=listing%3D0%2Abrowseby%3D8%2Asortby%3D2</identifier>
    <enrichment key="PeerReviewed">no</enrichment>
    <enrichment key="AcceptedDate">2020/04/29</enrichment>
    <author>Manish Sahu</author>
    <submitter>Manish Sahu</submitter>
    <author>Angelika Szengel</author>
    <author>Anirban Mukhopadhyay</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="institutes" number="MfLMS">Mathematics for Life and Materials Science</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="projects" number="BMBF-COMPASS">BMBF-COMPASS</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>6682</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>320</pageFirst>
    <pageLast>321</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>incollection</type>
    <publisherName>Seemann Henschel</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Face to Face-Interface</title>
    <parentTitle language="eng">+ultra. Knowledge &amp; Gestaltung</parentTitle>
    <enrichment key="PeerReviewed">no</enrichment>
    <author>Carl Martin Grewe</author>
    <editor>Nikola Doll</editor>
    <submitter>Carl Martin Grewe</submitter>
    <author>Stefan Zachow</author>
    <editor>Horst Bredekamp</editor>
    <editor>Wolfgang Schäffner</editor>
    <collection role="institutes" number="vis">Visual Data Analysis</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>
  </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>6515</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>203</pageFirst>
    <pageLast>204</pageLast>
    <pageNumber>2</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>17</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Adaptive Algorithms for Optimal Hip Implant Positioning</title>
    <abstract language="deu">In an aging society where the number of joint replacements rises, it is important to also increase the longevity of implants.&#13;
In particular hip implants have a lifetime of at most 15 years. This derives primarily from &#13;
pain due to implant migration, wear, inflammation, and dislocation, which is affected by &#13;
the positioning of the implant during the surgery. Current joint replacement practice uses &#13;
2D software tools and relies on the experience of surgeons. Especially the 2D tools fail to &#13;
take the patients’ natural range of motion as well as stress distribution in the 3D joint &#13;
induced by different daily motions into account.&#13;
Optimizing the hip joint implant position for all possible parametrized motions under the &#13;
constraint of a contact problem is prohibitively expensive as there are too many motions &#13;
and every position change demands a recalculation of the contact problem. For the &#13;
reduction of the computational effort, we use adaptive refinement on the parameter &#13;
domain coupled with the interpolation method of Kriging. A coarse initial grid is to be &#13;
locally refined using goal-oriented error estimation, reducing locally high  variances. This &#13;
approach will be combined with multi-grid optimization such that numerical errors are &#13;
reduced.</abstract>
    <parentTitle language="eng">PAMM</parentTitle>
    <identifier type="doi">10.1002/pamm.201710071</identifier>
    <enrichment key="PeerReviewed">no</enrichment>
    <author>Marian Moldenhauer</author>
    <submitter>Marian Moldenhauer</submitter>
    <author>Martin Weiser</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="persons" number="weiser">Weiser, Martin</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="ECMath-CH9">ECMath-CH9</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </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>8120</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>367</pageFirst>
    <pageLast>370</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Unsupervised Detection of Disturbances in 2D Radiographs</title>
    <abstract language="eng">We present a method based on a generative model for detection of disturbances such as prosthesis, screws, zippers, and metals in 2D radiographs. The generative model is trained in an unsupervised fashion using clinical radiographs as well as simulated data, none of which contain disturbances. Our approach employs a latent space consistency loss which has the benefit of identifying similarities, and is enforced to reconstruct X-rays without disturbances. In order to detect images with disturbances, an anomaly score is computed also employing the Frechet distance between the input X-ray and the reconstructed one using our generative model. Validation was performed using clinical pelvis radiographs. We achieved an AUC of 0.77 and 0.83 with clinical and synthetic data, respectively. The results demonstrated a good accuracy of our method for detecting outliers as well as the advantage of utilizing synthetic data.</abstract>
    <parentTitle language="eng">2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)</parentTitle>
    <identifier type="doi">10.1109/ISBI48211.2021.9434091</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Laura Estacio</author>
    <submitter>Alexander Tack</submitter>
    <author>Moritz Ehlke</author>
    <author>Alexander Tack</author>
    <author>Eveling Castro-Gutierrez</author>
    <author>Hans Lamecker</author>
    <author>Rensso Mora</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</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="BSRT-3DFromXray">BSRT-3DFromXray</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>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>8088</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>106080</issue>
    <volume>205</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-04-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Fully automated Assessment of Knee Alignment from Full-Leg X-Rays employing a "YOLOv4 And Resnet Landmark regression Algorithm" (YARLA): Data from the Osteoarthritis Initiative</title>
    <abstract language="deu">We present a method for the quantification of knee alignment from full-leg X-Rays. A state-of-the-art object detector, YOLOv4, was trained to locate regions of interests (ROIs) in full-leg X-Ray images for the hip joint, the knee, and the ankle. Residual neural networks (ResNets) were trained to regress landmark coordinates for each ROI.Based on the detected landmarks the knee alignment, i.e., the hip-knee-ankle (HKA) angle, was computed. The accuracy of landmark detection was evaluated by a comparison to manually placed landmarks for 360 legs in 180 X-Rays. The accuracy of HKA angle computations was assessed on the basis of 2,943 X-Rays. Results of YARLA were compared to the results of two independent image reading studies(Cooke; Duryea) both publicly accessible via the Osteoarthritis Initiative. The agreement was evaluated using Spearman's Rho, and weighted kappa as well as regarding the correspondence of the class assignment (varus/neutral/valgus). The average difference between YARLA and manually placed landmarks was less than 2.0+- 1.5 mm for all structures (hip, knee, ankle). The average mismatch between HKA angle determinations of Cooke and Duryea was 0.09 +- 0.63°; YARLA resulted in a mismatch of 0.10 +- 0.74° compared to Cooke and of  0.18 +- 0.64° compared to Duryea. Cooke and Duryea agreed almost perfectly with respect to a weighted kappa value of 0.86, and showed an excellent reliability as measured by a Spearman's Rho value of 0.99. Similar values were achieved by YARLA, i.e., a weighted kappa value of0.83 and 0.87 and a Spearman's Rho value of 0.98 and 0.99 to Cooke and Duryea,respectively. Cooke and Duryea agreed in 92% of all class assignments and YARLA did so in 90% against Cooke and 92% against Duryea. In conclusion, YARLA achieved results comparable to those of human experts and thus provides a basis for an automated assessment of knee alignment in full-leg X-Rays.</abstract>
    <parentTitle language="deu">Computer Methods and Programs in Biomedicine</parentTitle>
    <identifier type="doi">https://doi.org/10.1016/j.cmpb.2021.106080</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Alexander Tack</author>
    <submitter>Alexander Tack</submitter>
    <author>Bernhard Preim</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</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/8088/ZIBReport_21-28.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>
    <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="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="projects" number="MathPlus-TrU-1">MathPlus-TrU-1</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>8098</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>73</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-07-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Rigid Motion Invariant Statistical Shape Modeling based on Discrete Fundamental Forms</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. Additionally, as planar configurations form a submanifold in shape space, our representation allows for effective estimation of quasi-isometric surfaces flattenings. We evaluate the performance of our model w.r.t. shape-based classification of hippocampus and femur malformations due to Alzheimer's disease and osteoarthritis, respectively. In particular, we achieve state-of-the-art accuracies outperforming 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 biological shape variability, we carry out an analysis of specificity and generalization ability.</abstract>
    <parentTitle language="eng">Medical Image Analysis</parentTitle>
    <identifier type="doi">10.1016/j.media.2021.102178</identifier>
    <identifier type="arxiv">2111.06850</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2021/07/13</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="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>10127</id>
    <completedYear>2024</completedYear>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>12/2024</volume>
    <type>article</type>
    <publisherName>Frontiers Media SA</publisherName>
    <publisherPlace/>
    <creatingCorporation>Charité - Universitätsmedizin Berlin</creatingCorporation>
    <contributingCorporation>Zuse Institute Berlin (ZIB)</contributingCorporation>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Impact of the external knee flexion moment on patello-femoral loading derived from in vivo loads and kinematics</title>
    <abstract language="eng">Introduction: Anterior knee pain and other patello-femoral (PF) complications frequently limit the success of total knee arthroplasty as the final treatment of end stage osteoarthritis. However, knowledge about the in-vivo loading conditions at the PF joint remains limited, as no direct measurements are available. We hypothesised that the external knee flexion moment (EFM) is highly predictive of the PF contact forces during activities with substantial flexion of the loaded knee.&#13;
Materials and methods: Six patients (65–80 years, 67–101 kg) with total knee arthroplasty (TKA) performed two activities of daily living: sit-stand-sit and squat. Tibio-femoral (TF) contact forces were measured in vivo using instrumented tibial components, while synchronously internal TF and PF kinematics were captured with mobile fluoroscopy. The measurements were used to compute PF contact forces using patient specific musculoskeletal models. The relationship between the EFM and the PF contact force was quantified using linear regression.&#13;
Results: Mean peak TF contact forces of 1.97–3.24 times body weight (BW) were found while peak PF forces reached 1.75 to 3.29 times body weight (BW). The peak EFM ranged from 3.2 to 5.9 %BW times body height, and was a good predictor of the PF contact force (R2 = 0.95 and 0.88 for sit-stand-sit and squat, respectively).&#13;
Discussion: The novel combination of in vivo TF contact forces and internal patellar kinematics enabled a reliable assessment of PF contact forces. The results of the regression analysis suggest that PF forces can be estimated based solely on the EFM from quantitative gait analysis. Our study also demonstrates the relevance of PF contact forces, which reach magnitudes similar to TF forces during activities of daily living.</abstract>
    <parentTitle language="eng">Frontiers in Bioengineering and Biotechnology</parentTitle>
    <identifier type="issn">2296-4185</identifier>
    <identifier type="doi">10.3389/fbioe.2024.1473951</identifier>
    <identifier type="pmid">39881960</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_doi_json">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:51:04Z","timestamp":1753890664327,"version":"3.41.2"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T00:00:00Z","timestamp":1736899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["SFB1444 \/ 427826188 TR 1657\/1-1 DA 1786\/5-1 EH 422-2-1\/MO 3865-1-1"],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioeng. Biotechnol."],"abstract":"&lt;jats:sec&gt;&lt;jats:title&gt;Introduction&lt;\/jats:title&gt;&lt;jats:p&gt;Anterior knee pain and other patello-femoral (PF) complications frequently limit the success of total knee arthroplasty as the final treatment of end stage osteoarthritis. However, knowledge about the &lt;jats:italic&gt;in-vivo&lt;\/jats:italic&gt; loading conditions at the PF joint remains limited, as no direct measurements are available. We hypothesised that the external knee flexion moment (EFM) is highly predictive of the PF contact forces during activities with substantial flexion of the loaded knee.&lt;\/jats:p&gt;&lt;\/jats:sec&gt;&lt;jats:sec&gt;&lt;jats:title&gt;Materials and methods&lt;\/jats:title&gt;&lt;jats:p&gt;Six patients (65\u201380\u00a0years, 67\u2013101\u00a0kg) with total knee arthroplasty (TKA) performed two activities of daily living: sit-stand-sit and squat. Tibio-femoral (TF) contact forces were measured &lt;jats:italic&gt;in vivo&lt;\/jats:italic&gt; using instrumented tibial components, while synchronously internal TF and PF kinematics were captured with mobile fluoroscopy. The measurements were used to compute PF contact forces using patient specific musculoskeletal models. The relationship between the EFM and the PF contact force was quantified using linear regression.&lt;\/jats:p&gt;&lt;\/jats:sec&gt;&lt;jats:sec&gt;&lt;jats:title&gt;Results&lt;\/jats:title&gt;&lt;jats:p&gt;Mean peak TF contact forces of 1.97\u20133.24 times body weight (BW) were found while peak PF forces reached 1.75 to 3.29 times body weight (BW). The peak EFM ranged from 3.2 to 5.9 %BW times body height, and was a good predictor of the PF contact force (&lt;jats:italic&gt;R&lt;\/jats:italic&gt;&lt;jats:sup&gt;2&lt;\/jats:sup&gt; = 0.95 and 0.88 for sit-stand-sit and squat, respectively).&lt;\/jats:p&gt;&lt;\/jats:sec&gt;&lt;jats:sec&gt;&lt;jats:title&gt;Discussion&lt;\/jats:title&gt;&lt;jats:p&gt;The novel combination of &lt;jats:italic&gt;in vivo&lt;\/jats:italic&gt; TF contact forces and internal patellar kinematics enabled a reliable assessment of PF contact forces. The results of the regression analysis suggest that PF forces can be estimated based solely on the EFM from quantitative gait analysis. Our study also demonstrates the relevance of PF contact forces, which reach magnitudes similar to TF forces during activities of daily living.&lt;\/jats:p&gt;&lt;\/jats:sec&gt;","DOI":"10.3389\/fbioe.2024.1473951","type":"journal-article","created":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T06:12:11Z","timestamp":1736921531000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Impact of the external knee flexion moment on patello-femoral loading derived from in vivo loads and kinematics"],"prefix":"10.3389","volume":"12","author":[{"given":"Adam","family":"Trepczynski","sequence":"first","affiliation":[]},{"given":"Paul","family":"Kneifel","sequence":"additional","affiliation":[]},{"given":"Mark","family":"Heyland","sequence":"additional","affiliation":[]},{"given":"Marko","family":"Leskovar","sequence":"additional","affiliation":[]},{"given":"Philippe","family":"Moewis","sequence":"additional","affiliation":[]},{"given":"Philipp","family":"Damm","sequence":"additional","affiliation":[]},{"given":"William R.","family":"Taylor","sequence":"additional","affiliation":[]},{"given":"Stefan","family":"Zachow","sequence":"additional","affiliation":[]},{"given":"Georg N.","family":"Duda","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,1,15]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"104012","DOI":"10.1016\/j.compbiomed.2020.104012","article-title":"Computational frame of ligament in situ strain in a full knee model","volume":"126","author":"Adouni","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1007\/s10237-019-01159-9","article-title":"A multiscale synthesis: characterizing acute cartilage failure under an aggregate tibiofemoral joint loading","volume":"18","author":"Adouni","year":"2019","journal-title":"Biomech. Model. Mechanobiol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1007\/s00590-019-02499-z","article-title":"Patellar complications following total knee arthroplasty: a review of the current literature","volume":"29","author":"Assiotis","year":"2019","journal-title":"Eur. J. Orthop. Surg. Traumatol."},{"key":"B4","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1007\/s11999-009-1046-9","article-title":"Comparing patient outcomes after THA and TKA: is there a difference?","volume":"468","author":"Bourne","year":"2010","journal-title":"Clin. Orthop. Relat. Res."},{"key":"B5","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.knee.2004.05.006","article-title":"Patellofemoral forces after total knee arthroplasty: effect of extensor moment arm","volume":"12","author":"Browne","year":"2005","journal-title":"Knee"},{"key":"B6","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1007\/s00167-010-1218-x","article-title":"Three-to six-year follow-up results after high-flexion total knee arthroplasty: can we allow passive deep knee bending?","volume":"19","author":"Cho","year":"2011","journal-title":"Knee Surg. Sports Traumatol. Arthrosc."},{"key":"B7","doi-asserted-by":"publisher","first-page":"2986","DOI":"10.1016\/j.arth.2021.03.053","article-title":"Patellar fracture after total knee arthroplasty with retention: a retrospective analysis of 2954 consecutive cases","volume":"36","author":"Choe","year":"2021","journal-title":"J. Arthroplasty"},{"key":"B8","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1243\/emed_jour_1982_011_019_02","article-title":"Forces during squatting and rising from a deep squat","volume":"11","author":"Dahlkvist","year":"1982","journal-title":"Eng. Med."},{"key":"B9","doi-asserted-by":"publisher","first-page":"85","DOI":"10.2106\/00004623-200300004-00010","article-title":"Impact of patellofemoral design on patellofemoral forces and polyethylene stresses","volume":"85","author":"D'Lima","year":"2003","journal-title":"J. Bone Jt. Surg. Am."},{"key":"B10","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1016\/j.jbiomech.2010.12.009","article-title":"The SCoRE residual: a quality index to assess the accuracy of joint estimations","volume":"44","author":"Ehrig","year":"2011","journal-title":"J. Biomech."},{"key":"B11","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1007\/s10237-018-01112-2","article-title":"The effect of fibrillar degradation on the mechanics of articular cartilage: a computational model","volume":"18","author":"Faisal","year":"2019","journal-title":"Biomech. Model. Mechanobiol."},{"key":"B12","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1302\/0301-620x.89b11.19840","article-title":"High incidence of loosening of the femoral component in legacy posterior stabilised-flex total knee replacement","volume":"89","author":"Han","year":"2007","journal-title":"J. Bone Jt. Surg. Br."},{"key":"B13","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511811685","volume-title":"Multiple view geometry in computer vision","author":"Hartley","year":"2004"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1284091","DOI":"10.3389\/fbioe.2023.1284091","article-title":"Lower-limb internal loading and potential consequences for fracture healing","volume":"11","author":"Heyland","year":"2023","journal-title":"Front. Bioeng. Biotechnol."},{"key":"B15","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1148\/101.1.101","article-title":"Patella position in the normal knee joint","volume":"101","author":"Insall","year":"1971","journal-title":"Radiology"},{"volume-title":"14243-5: implants for surgery - wear of total knee prostheses - durability performance of the patellofemoral joint","year":"2019","key":"B16"},{"key":"B17","doi-asserted-by":"publisher","first-page":"111549","DOI":"10.1016\/j.jbiomech.2023.111549","article-title":"Patellar tendon elastic properties derived from in vivo loading and kinematics","volume":"151","author":"Kneifel","year":"2023","journal-title":"J. Biomech."},{"key":"B18","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.jbiomech.2004.02.041","article-title":"Knee mechanics: a review of past and present techniques to determine in vivo loads","volume":"38","author":"Komistek","year":"2005","journal-title":"J. Biomech."},{"key":"B19","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1097\/01.blo.0000238803.97713.7d","article-title":"Patella maltracking in posterior-stabilized total knee arthroplasty","volume":"452","author":"Lachiewicz","year":"2006","journal-title":"Clin. Orthop. Relat. Res."},{"key":"B20","doi-asserted-by":"publisher","first-page":"e0185952","DOI":"10.1371\/journal.pone.0185952","article-title":"A moving fluoroscope to capture tibiofemoral kinematics during complete cycles of free level and downhill walking as well as stair descent","volume":"12","author":"List","year":"2017","journal-title":"PLoS One"},{"key":"B21","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1016\/j.jbiomech.2008.04.039","article-title":"Patellofemoral joint forces","volume":"41","author":"Mason","year":"2008","journal-title":"J. Biomech."},{"key":"B22","doi-asserted-by":"publisher","first-page":"2765","DOI":"10.1007\/s00590-023-03535-9","article-title":"Periprosthetic patella fractures in total knee replacement and revision surgeries: how to diagnose and treat this rare but potentially devastating complication-a review of the current literature","volume":"33","author":"Masoni","year":"2023","journal-title":"Eur. J. Orthop. Surg. Traumatol."},{"key":"B23","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.jmbbm.2018.06.035","article-title":"Loading and kinematic profiles for patellofemoral durability testing","volume":"86","author":"Navacchia","year":"2018","journal-title":"J. Mech. Behav. Biomed. Mater"},{"key":"B24","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1097\/01.blo.0000150130.03519.fb","article-title":"Does total knee replacement restore normal knee function?","volume":"431","author":"Noble","year":"2005","journal-title":"Clin. Orthop. Relat. Res."},{"key":"B25","doi-asserted-by":"publisher","first-page":"532","DOI":"10.2106\/00004623-200204000-00004","article-title":"Patellar fracture after total knee arthroplasty","volume":"84","author":"Ortiguera","year":"2002","journal-title":"J. Bone Jt. Surg. Am."},{"key":"B26","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/s00264-013-2081-4","article-title":"Anterior knee pain after total knee arthroplasty: a narrative review","volume":"38","author":"Petersen","year":"2014","journal-title":"Int. Orthop."},{"key":"B27","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1302\/0301-620x.92b8.23794","article-title":"The measurement of patellar height: a review of the methods of imaging","volume":"92","author":"Phillips","year":"2010","journal-title":"J. Bone Jt. Surg. Br."},{"volume-title":"R: a language and environment for statistical computing","year":"2022","key":"B28"},{"key":"B29","doi-asserted-by":"publisher","first-page":"126","DOI":"10.3109\/17453677208991251","article-title":"Experimental analysis of the quadriceps muscle force and patello-femoral joint reaction force for various activities","volume":"43","author":"Reilly","year":"1972","journal-title":"Acta Orthop. Scand."},{"key":"B30","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1016\/s0883-5403(96)80024-6","article-title":"Patellofemoral complications following total knee arthroplasty","volume":"11","author":"Ritter","year":"1996","journal-title":"J. Arthroplasty"},{"key":"B31","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1615\/jlongtermeffmedimplants.2013010099","article-title":"Patellar fractures following total knee arthroplasty: a review","volume":"23","author":"Sayeed","year":"2013","journal-title":"J. Long. Term. Eff. Med. Implants"},{"key":"B32","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1016\/j.arth.2011.01.016","article-title":"Patellofemoral function after total knee arthroplasty: gender-related differences","volume":"26","author":"Sensi","year":"2011","journal-title":"J. Arthroplasty"},{"key":"B33","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1016\/j.jbiomech.2007.09.027","article-title":"In vivo patellofemoral forces in high flexion total knee arthroplasty","volume":"41","author":"Sharma","year":"2008","journal-title":"J. Biomech."},{"key":"B34","doi-asserted-by":"publisher","first-page":"2110","DOI":"10.1177\/03635465231175160","article-title":"Patellofemoral joint loading progression across 35 weightbearing rehabilitation exercises and activities of daily living","volume":"51","author":"Song","year":"2023","journal-title":"Am. J. Sports Med."},{"key":"B35","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.gaitpost.2010.05.005","article-title":"Repeatability and reproducibility of OSSCA, a functional approach for assessing the kinematics of the lower limb","volume":"32","author":"Taylor","year":"2010","journal-title":"Gait Posture"},{"key":"B36","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.jbiomech.2017.09.022","article-title":"A comprehensive assessment of the musculoskeletal system: the CAMS-Knee data set","volume":"65","author":"Taylor","year":"2017","journal-title":"J. Biomech."},{"key":"B37","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1002\/jor.21540","article-title":"Patellofemoral joint contact forces during activities with high knee flexion","volume":"30","author":"Trepczynski","year":"2012","journal-title":"J. Orthop. Res."},{"key":"B38","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1038\/s41598-018-37189-z","article-title":"Tibio-femoral contact force distribution is not the only factor governing pivot location after total knee arthroplasty","volume":"9","author":"Trepczynski","year":"2019","journal-title":"Sci. Rep."},{"key":"B39","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1186\/s12984-018-0434-3","article-title":"Impact of antagonistic muscle co-contraction on in vivo knee contact forces","volume":"15","author":"Trepczynski","year":"2018","journal-title":"J. Neuroeng Rehabil."},{"key":"B40","doi-asserted-by":"publisher","first-page":"754715","DOI":"10.3389\/fbioe.2021.754715","article-title":"Dynamic knee joint line orientation is not predictive of tibio-femoral load distribution during walking","volume":"9","author":"Trepczynski","year":"2021","journal-title":"Front. Bioeng. Biotechnol."},{"key":"B41","doi-asserted-by":"publisher","first-page":"e53281","DOI":"10.7759\/cureus.53281","article-title":"Patella fracture after total knee arthroplasty: a review","volume":"16","author":"Tsivelekas","year":"2024","journal-title":"Cureus"}],"container-title":["Frontiers in Bioengineering and Biotechnology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbioe.2024.1473951\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T06:12:17Z","timestamp":1736921537000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbioe.2024.1473951\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,15]]},"references-count":41,"alternative-id":["10.3389\/fbioe.2024.1473951"],"URL":"https:\/\/doi.org\/10.3389\/fbioe.2024.1473951","relation":{},"ISSN":["2296-4185"],"issn-type":[{"type":"electronic","value":"2296-4185"}],"subject":[],"published":{"date-parts":[[2025,1,15]]},"article-number":"1473951"}}</enrichment>
    <enrichment key="opus_crossrefLicence">https://creativecommons.org/licenses/by/4.0/</enrichment>
    <enrichment key="opus_import_origin">crossref</enrichment>
    <enrichment key="opus_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,PersonAuthorFirstName_8,PersonAuthorLastName_8,PersonAuthorFirstName_9,PersonAuthorLastName_9,PublisherName,TitleMain_1,TitleAbstract_1,TitleParent_1,ArticleNumber,Volume,PublishedYear,IdentifierIssn,Enrichmentopus_crossrefLicence</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <author>Adam Trepczynski</author>
    <submitter>Stefan Zachow</submitter>
    <author>Paul Kneifel</author>
    <author>Mark Heyland</author>
    <author>Marko Leskovar</author>
    <author>Philippe Moewis</author>
    <author>Philipp Damm</author>
    <author>William R. Taylor</author>
    <author>Stefan Zachow</author>
    <author>Georg N. Duda</author>
    <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>
    <collection role="projects" number="SFB_1444">SFB_1444</collection>
    <collection role="projects" number="SFB1444">SFB1444</collection>
  </doc>
  <doc>
    <id>9822</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2024-12-09</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Surgical planning in HTO – Alternative approaches to the Fujisawa gold-standard</title>
    <abstract language="eng">BACKGROUND: Presurgical planning of the correction angle plays a&#13;
decisive role in a high tibial osteotomy, affecting the loading situation in&#13;
the knee affected by osteoarthritis. The planning approach by Fujisawa et&#13;
al. aims to adjust the weight-bearing line to achieve an optimal knee joint&#13;
load distribution. While this method is accessible, it may not fully&#13;
consider the complexity of individual dynamic knee-loading profiles. This&#13;
review aims to disclose existing alternative HTO planning methods that&#13;
do not follow Fujisawa’s standard.&#13;
METHODS: PubMed, Web of Science and CENTRAL databases were&#13;
screened, focusing on HTO research in combination with alternative&#13;
planning approaches.&#13;
RESULTS: Eight out of 828 studies were included, with seven simulation&#13;
studies based on finite element analysis and multi-body dynamics. The&#13;
planning approaches incorporated gradual degrees of realignment&#13;
parameters (weight-bearing line shift, medial proximal tibial angle, hip-&#13;
knee-ankle, knee joint line orientation), simulating their effect on knee&#13;
kinematics, contact force/stress, Von Mises and shear stress. Two studies&#13;
proposed implementing individual correction magnitudes derived from&#13;
preoperatively predicted knee adduction moments.&#13;
CONCLUSION: Most planning methods depend on static alignment&#13;
assessments, neglecting an adequate loading-depending profile. They are&#13;
confined to their conceptual phases, making the associated planning&#13;
methods unviable for current clinical use.</abstract>
    <parentTitle language="eng">Technology and Health Care</parentTitle>
    <identifier type="doi">10.1177/09287329241299568</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-98227</identifier>
    <enrichment key="AcceptedDate">18.10.2024</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Igor Komnik</author>
    <author>Johannes Funken</author>
    <author>Stefan Zachow</author>
    <author>Rüdiger Schmidt-Wiethoff</author>
    <author>Andree Ellermann</author>
    <author>Wolfgang Potthast</author>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="projects" number="MoA_Cutting">MoA_Cutting</collection>
    <collection role="projects" number="SFB_1444">SFB_1444</collection>
  </doc>
</export-example>
