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Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
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.
AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design.
IEEE Trans Med Imaging. 2021; 40(9):2329-2342
Doi: 10.1109/TMI.2021.3077047
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PubMed
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- Co-authors Med Uni Graz
- Egger Jan
- Pistracher Karin Felicitas
- Schäfer Ute
- Schwarz-Gsaxner Christina
- von Campe Gord
- Zefferer Ulrike
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- 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.
- Find related publications in this database (Keywords)
- Skull
- Shape
- Implants
- Three-dimensional displays
- Cranial
- Image reconstruction
- Biomedical imaging
- Volumetric shape completion
- shape inpainting
- skull reconstruction
- shape prior
- statistical shape model
- deep learning
- cranioplasty