AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design

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 evalu...

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Veröffentlicht in:	IEEE transactions on medical imaging 2021-09, Vol.40 (9), p.2329-2342
Hauptverfasser:	Li, Jianning, Pimentel, Pedro, Szengel, Angelika, Ehlke, Moritz, Lamecker, Hans, Zachow, Stefan, Estacio, Laura, Doenitz, Christian, Ramm, Heiko, Shi, Haochen, Chen, Xiaojun, Matzkin, Franco, Newcombe, Virginia, Ferrante, Enzo, Jin, Yuan, Ellis, David G., Aizenberg, Michele R., Kodym, Oldrich, Spanel, Michal, Herout, Adam, Mainprize, James G., Fishman, Zachary, Hardisty, Michael R., Bayat, Amirhossein, Shit, Suprosanna, Wang, Bomin, Liu, Zhi, Eder, Matthias, Pepe, Antonio, Gsaxner, Christina, Alves, Victor, Zefferer, Ulrike, von Campe, Gord, Pistracher, Karin, Schafer, Ute, Schmalstieg, Dieter, Menze, Bjoern H., Glocker, Ben, Egger, Jan
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Biomedical imaging Cranial cranioplasty Deep learning Image reconstruction Implants Prostheses and Implants Shape shape inpainting shape prior Skull Skull - diagnostic imaging Skull - surgery skull reconstruction statistical shape model Three-dimensional displays Volumetric shape completion
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Beschreibung
Zusammenfassung:	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. The codes can be found at https://github.com/Jianningli/tmi .
ISSN:	0278-0062 1558-254X
DOI:	10.1109/TMI.2021.3077047