1. AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design
- Author
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Ute Schäfer, Gord von Campe, Yuan Jin, Hans Lamecker, Bomin Wang, Michal Spanel, Christian Doenitz, Heiko Ramm, Ulrike Zefferer, Matthias Josef Eder, Pedro Pimentel, Angelika Szengel, Adam Herout, Victor Alves, Oldřich Kodym, Franco Matzkin, Jan Egger, Moritz Ehlke, Zachary Fishman, James G. Mainprize, Ben Glocker, Stefan Zachow, Jianning Li, Virginia F. J. Newcombe, David G. Ellis, Dieter Schmalstieg, Haochen Shi, Enzo Ferrante, Karin Pistracher, Zhi Liu, Antonio Pepe, Bjoern H. Menze, Michele R. Aizenberg, Amirhossein Bayat, Christina Gsaxner, Suprosanna Shit, Michael Hardisty, Laura Estacio, and Xiaojun Chen
- Subjects
Radiological and Ultrasound Technology ,business.industry ,Computer science ,Deep learning ,medicine.medical_treatment ,Skull ,MEDLINE ,Prostheses and Implants ,Data science ,Cranioplasty ,Computer Science Applications ,Fully automated ,Autoimplant ,Medical imaging ,medicine ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Software ,Cranial implant - Abstract
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 .
- Published
- 2021