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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Detalhes bibliográficos
Autor principal: Li, Jianning (author)
Outros Autores: Pimentel, Pedro (author), Szengel, Angelika (author), Ehlke, Moritz (author), Lamecker, Hans (author), Zachow, Stefan (author), Estacio, Laura (author), Doenitz, Christian (author), Ramm, Heiko (author), Shi, Haochen (author), Chen, Xiaojun (author), Matzkin, Franco (author), Newcombe, Virginia (author), Ferrante, Enzo (author), Jin, Yuan (author), Ellis, David G. (author), Aizenberg, Michele R. (author), Kodym, Oldrich (author), Spanel, Michal (author), Herout, Adam (author), Mainprize, James G. (author), Fishman, Zachary (author), Hardisty, Michael R. (author), Bayat, Amirhossein (author), Shit, Suprosanna (author), Wang, Bomin (author), Liu, Zhi (author), Eder, Matthias (author), Pepe, Antonio (author), Gsaxner, Christina (author), Alves, Victor (author), Zefferer, Ulrike (author), Von Campe, Gord (author), Pistracher, Karin (author), Schafer, Ute (author), Schmalstieg, Dieter (author), Menze, Bjoern H. (author), Glocker, Ben (author), Egger, Jan (author)
Formato: article
Idioma:eng
Publicado em: 2021
Assuntos:
cranioplasty
deep learning
shape inpainting
shape prior
skull reconstruction
statistical shape model
Volumetric shape completion
Skull
Shape
Implants
Three-dimensional displays
Cranial
Image reconstruction
Biomedical imaging
Science & Technology
Texto completo:https://hdl.handle.net/1822/78123
País:Portugal
Oai:oai:repositorium.sdum.uminho.pt:1822/78123
Descrição
Resumo: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.

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