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

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Jianning Li, Jan Egger
Springer Nature, 04.12.2020 - 115 Seiten
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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 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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Inhalt

Patient Specific Implants PSI
1
Dataset Descriptor for the AutoImplant Cranial Implant Design Challenge
10
Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks
16
Cranial Implant Design Through Multiaxial Slice Inpainting Using Deep Learning
28
Cranial Implant Design via Virtual Craniectomy with Shape Priors
37
1st Place Solution to the AutoImplant 2020 Challenge
47
Cranial Defect Reconstruction Using Cascaded CNN with Alignment
56
An Approach to the AutoImplant MICCAI Cranial Implant Design Challenge
65
Cranial Implant Prediction Using LowResolution 3D Shape Completion and HighResolution 2D Refinement
77
Cranial Implant Design Using a Deep Learning Method with Anatomical Regularization
85
HighResolution Cranial Implant Prediction via PatchWise Training
94
Learning Volumetric Shape SuperResolution for Cranial Implant Design
104
Author Index
114
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