AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design

Jianning Li, Pedro Pimentel, Angelika Szengel, Moritz Ehlke, Hans Lamecker, Stefan Zachow, Laura Estacio, Christian Doenitz, Heiko Ramm, Haochen Shi, Xiaojun Chen, Franco Matzkin, Virginia Newcombe, Enzo Ferrante, Yuan Jin, David G. Ellis, Michele R. Aizenberg, Oldrich Kodym, Michal Spanel, Adam HeroutJames G. Mainprize, Zachary Fishman, Michael R. Hardisty, Amirhossein Bayat, Suprosanna Shit, Bomin Wang, Zhi Liu, Matthias Eder, Antonio Pepe, Christina Gsaxner, Victor Alves, Ulrike Zefferer, Gord Von Campe, Karin Pistracher, Ute Schafer, Dieter Schmalstieg, Bjoern H. Menze, Ben Glocker, Jan Egger

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish (US)
Article number9420655
Pages (from-to)2329-2342
Number of pages14
JournalIEEE transactions on medical imaging
Volume40
Issue number9
DOIs
StatePublished - Sep 2021

Keywords

  • Volumetric shape completion
  • cranioplasty
  • deep learning
  • shape inpainting
  • shape prior
  • skull reconstruction
  • statistical shape model

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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