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 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.Fil: Li, Jianning. Technische Universitat Graz; AustriaFil: Pimentel, Pedro. No especifíca;Fil: Szengel, Angelika. No especifíca;Fil: Ehlke, Moritz. No especifíca;Fil: Lamecker, Hans. No especifíca;Fil: Zachow, Stefan. No especifíca;Fil: Estacio, Laura. Universidad Católica San Pablo; PerúFil: Doenitz, Christian. No especifíca;Fil: Ramm, Heiko. No especifíca;Fil: Shi, Haochen. Shanghai Jiao Tong University; ChinaFil: Chen, Xiaojun. Shanghai Jiao Tong University; ChinaFil: Matzkin, Victor Franco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Newcombe, Virginia. University of Cambridge; Estados UnidosFil: Ferrante, Enzo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Jin, Yuan. Technische Universitat Graz; AustriaFil: Ellis, David G.. No especifíca;Fil: Aizenberg, Michele R.. University of Nebraska; Estados UnidosFil: Kodym, Oldrich. No especifíca;Fil: Spanel, Michal. No especifíca;Fil: Herout, Adam. No especifíca;Fil: Mainprize, James G.. Sunnybrook Health Sciences Centre; CanadáFil: Fishman, Zachary. Sunnybrook Health Sciences Centre; CanadáFil: Hardisty, Michael R.. Sunnybrook Health Sciences Centre; CanadáFil: Bayat, Amirhossein. No especifíca;Fil: Shit, Suprosanna. No especifíca;Fil: Wang, Bomin. Shandong University; ChinaFil: Liu, Zhi. Shandong University; ChinaFil: Eder, Matthias. Technische Universitat Graz; AustriaFil: Pepe, Antonio. Technische Universitat Graz; AustriaFil: Gsaxner, Christina. Technische Universitat Graz; AustriaFil: Alves, Victor. Universidade do Minho; PortugalFil: Zefferer, Ulrike. Medizinische Universität Graz; AustriaFil: Von Campe, Gord. Medizinische Universität Graz; AustriaFil: Pistracher, Karin. Medizinische Universität Graz; AustriaFil: Schafer, Ute. Medizinische Universität Graz; AustriaFil: Schmalstieg, Dieter. Technische Universitat Graz; AustriaFil: Menze, Bjoern H.. No especifíca;Fil: Glocker, Ben. Imperial College London; Reino UnidoFil: Egger, Jan. Computer Algorithms For Medicine Laboratory; Austri

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 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

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 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.FWF - Austrian Science Fund(KLI 678-B31