Towards the Automatization of Cranial Implant Design in Cranioplasty
First Challenge, AutoImplant 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings
Herausgegeben: Li, Jianning; Egger, Jan
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This book constitutes the First Automatization of Cranial Implant Design in Cranioplasty Challenge, AutoImplant 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took place virtually due to the COVID-19 pandemic.The 10 papers presented together with one invited paper and a dataset descriptor in this volume were carefully reviewed and selected form numerous submissions. This challenge aims to provide more affordable, faster, and more patient-friendly...
This book constitutes the First Automatization of Cranial Implant Design in Cranioplasty Challenge, AutoImplant 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took place virtually due to the COVID-19 pandemic.
The 10 papers presented together with one invited paper and a dataset descriptor in this volume were carefully reviewed and selected form numerous submissions. This challenge aims to provide more affordable, faster, and more patient-friendly solutions to the design and manufacturing of medical implants, including cranial implants, which is needed in order to repair a defective skull from a brain tumor surgery or trauma. The presented solutions can serve as a good benchmark for future publications regarding 3D volumetric shape learning and cranial implant design.
The 10 papers presented together with one invited paper and a dataset descriptor in this volume were carefully reviewed and selected form numerous submissions. This challenge aims to provide more affordable, faster, and more patient-friendly solutions to the design and manufacturing of medical implants, including cranial implants, which is needed in order to repair a defective skull from a brain tumor surgery or trauma. The presented solutions can serve as a good benchmark for future publications regarding 3D volumetric shape learning and cranial implant design.
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