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<export-example>
  <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>8225</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2329</pageFirst>
    <pageLast>2342</pageLast>
    <pageNumber/>
    <edition/>
    <issue>9</issue>
    <volume>40</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-09-09</completedDate>
    <publishedDate>2021-05-03</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">AutoImplant 2020 - First MICCAI Challenge on Automatic Cranial Implant Design</title>
    <abstract language="eng">The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use.</abstract>
    <parentTitle language="eng">IEEE Transactions on Medical Imaging</parentTitle>
    <identifier type="doi">10.1109/TMI.2021.3077047</identifier>
    <identifier type="issn">0278-0062</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2021-04-28</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Jianning Li</author>
    <submitter>Stefan Zachow</submitter>
    <author>Pedro Pimentel</author>
    <author>Angelika Szengel</author>
    <author>Moritz Ehlke</author>
    <author>Hans Lamecker</author>
    <author>Stefan Zachow</author>
    <author>Laura Estacio</author>
    <author>Christian Doenitz</author>
    <author>Heiko Ramm</author>
    <author>Haochen Shi</author>
    <author>Xiaojun Chen</author>
    <author>Franco Matzkin</author>
    <author>Virginia Newcombe</author>
    <author>Enzo Ferrante</author>
    <author>Yuan Jin</author>
    <author>David G. Ellis</author>
    <author>Michele R. Aizenberg</author>
    <author>Oldrich Kodym</author>
    <author>Michal Spanel</author>
    <author>Adam Herout</author>
    <author>James G. Mainprize</author>
    <author>Zachary Fishman</author>
    <author>Michael R. Hardisty</author>
    <author>Amirhossein Bayat</author>
    <author>Suprosanna Shit</author>
    <author>Bomin Wang</author>
    <author>Zhi Liu</author>
    <author>Matthias Eder</author>
    <author>Antonio Pepe</author>
    <author>Christina Gsaxner</author>
    <author>Victor Alves</author>
    <author>Ulrike Zefferer</author>
    <author>Cord von Campe</author>
    <author>Karin Pistracher</author>
    <author>Ute Schäfer</author>
    <author>Dieter Schmalstieg</author>
    <author>Bjoern H. Menze</author>
    <author>Ben Glocker</author>
    <author>Jan Egger</author>
    <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="Cranio">Cranio</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Modal-Seg">Modal-Seg</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="MfLMS">Mathematics for Life and Materials Science</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
</export-example>
