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 Herout; James 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

doi:10.1109/TMI.2021.3077047

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

^*Corresponding author for this work

Institute of Computer Graphics and Vision (7100)

Research output: Contribution to journal › Review article › peer-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 language	English
Article number	9420655
Pages (from-to)	2329-2342
Number of pages	14
Journal	IEEE Transactions on Medical Imaging
Volume	40
Issue number	9
DOIs	https://doi.org/10.1109/TMI.2021.3077047
Publication status	Published - Sept 2021

Keywords

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

ASJC Scopus subject areas

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

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10.1109/TMI.2021.3077047

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Li, J., Pimentel, P., Szengel, A., Ehlke, M., Lamecker, H., Zachow, S., Estacio, L., Doenitz, C., Ramm, H., Shi, H., Chen, X., Matzkin, F., Newcombe, V., Ferrante, E., Jin, Y., Ellis, D. G., Aizenberg, M. R., Kodym, O., Spanel, M., ... Egger, J. (2021). AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design. IEEE Transactions on Medical Imaging, 40(9), 2329-2342. [9420655]. https://doi.org/10.1109/TMI.2021.3077047

Li, J, Pimentel, P, Szengel, A, Ehlke, M, Lamecker, H, Zachow, S, Estacio, L, Doenitz, C, Ramm, H, Shi, H, Chen, X, Matzkin, F, Newcombe, V, Ferrante, E, Jin, Y, Ellis, DG, Aizenberg, MR, Kodym, O, Spanel, M, Herout, A, Mainprize, JG, Fishman, Z, Hardisty, MR, Bayat, A, Shit, S, Wang, B, Liu, Z, Eder, M , Pepe, A , Gsaxner, C, Alves, V, Zefferer, U, Von Campe, G, Pistracher, K, Schafer, U, Schmalstieg, D, Menze, BH, Glocker, B & Egger, J 2021, 'AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design', IEEE Transactions on Medical Imaging, vol. 40, no. 9, 9420655, pp. 2329-2342. https://doi.org/10.1109/TMI.2021.3077047

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title = "AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design",

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. ",

keywords = "cranioplasty, deep learning, shape inpainting, shape prior, skull reconstruction, statistical shape model, Volumetric shape completion",

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

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doi = "10.1109/TMI.2021.3077047",

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AU - Li, Jianning

AU - Pimentel, Pedro

AU - Szengel, Angelika

AU - Ehlke, Moritz

AU - Lamecker, Hans

AU - Zachow, Stefan

AU - Estacio, Laura

AU - Doenitz, Christian

AU - Ramm, Heiko

AU - Shi, Haochen

AU - Chen, Xiaojun

AU - Matzkin, Franco

AU - Newcombe, Virginia

AU - Ferrante, Enzo

AU - Jin, Yuan

AU - Ellis, David G.

AU - Aizenberg, Michele R.

AU - Kodym, Oldrich

AU - Spanel, Michal

AU - Herout, Adam

AU - Mainprize, James G.

AU - Fishman, Zachary

AU - Hardisty, Michael R.

AU - Bayat, Amirhossein

AU - Shit, Suprosanna

AU - Wang, Bomin

AU - Liu, Zhi

AU - Eder, Matthias

AU - Pepe, Antonio

AU - Gsaxner, Christina

AU - Alves, Victor

AU - Zefferer, Ulrike

AU - Von Campe, Gord

AU - Pistracher, Karin

AU - Schafer, Ute

AU - Schmalstieg, Dieter

AU - Menze, Bjoern H.

AU - Glocker, Ben

AU - Egger, Jan

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AB - 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.

KW - cranioplasty

KW - deep learning

KW - shape inpainting

KW - shape prior

KW - skull reconstruction

KW - statistical shape model

KW - Volumetric shape completion

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AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design

Abstract

Keywords

ASJC Scopus subject areas

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