Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networ
We present an automated method for extrapolating missing regions in label data of the skull in an anatomically plausible manner. The ultimate goal is to design patient-specific cranial implants for correcting large, arbitrarily shaped defects of the skull
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Jianning Li Jan Egger (Eds.)
Towards the Automatization of Cranial Implant Design in Cranioplasty First Challenge, AutoImplant 2020 Held in Conjunction with MICCAI 2020 Lima, Peru, October 8, 2020, Proceedings
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
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Jianning Li Jan Egger (Eds.) •
Towards the Automatization of Cranial Implant Design in Cranioplasty First Challenge, AutoImplant 2020 Held in Conjunction with MICCAI 2020 Lima, Peru, October 8, 2020 Proceedings
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Editors Jianning Li Graz University of Technology Graz, Austria
Jan Egger Graz University of Technology Graz, Austria
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-64326-3 ISBN 978-3-030-64327-0 (eBook) https://doi.org/10.1007/978-3-030-64327-0 LNCS Sublibrary: SL6 – Image Processing, Computer Vision, Pattern Recognition, and Graphics © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
The AutoImplant Cranial Implant Design Challenge (AutoImplant 2020: https:// autoimplant.grand-challenge.org/) was initialized jointly by the Graz University of Technology (TU Graz) and the Medical University of Graz (MedUni Graz), Austria, through an interdisciplinary proj
