Science and Technology Production
AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design
Article
Authorship:
Li, Jianning ; Pimentel, Pedro ; Szengel, Angelika ; Ehlke, Moritz ; Lamecker, Hans ; Zachow, Stefan ; Estacio, Laura ; Doenitz, Christian ; Ramm, Heiko ; Shi, Haochen ; Chen, Xiaojun ; Matzkin, Victor 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, JanDate:
2021Publishing House and Editing Place:
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCMagazine:
IEEE TRANSACTION ON MEDICAL IMAGING, vol. 40 (pp. 2329-2342) IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCSummary
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.Key Words
SHAPE PRIORSHAPE INPAINTINGVOLUMETRIC SHAPE COMPLETIONDEEP LEARNINGSTATISTICAL SHAPE MODELCRANIOPLASTYSKULL RECONSTRUCTION