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

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.

Patient Specific Implants (PSI): Cranioplasty in the Neurosurgical Clinical Routine.- Dataset Descriptor for the AutoImplant Cranial Implant Design Challenge.- Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks.- Cranial Implant Design through Multiaxial Slice Inpainting using Deep Learning.- Cranial Implant Design via Virtual Craniectomy with Shape Priors.- Deep Learning Using Augmentation via Registration: 1st Place Solution to the AutoImplant 2020 Challenge.- Cranial Defect Reconstruction using Cascaded CNN with Alignment.- Shape Completion by U-Net: An Approach to the AutoImplant MICCAI Cranial Implant Design Challenge.- Cranial Implant Prediction using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement.- Cranial Implant Design Using a Deep Learning Method with Anatomical Regularization.- High-resolution Cranial Implant Prediction via Patch-wise Training.- Learning Volumetric Shape Super-Resolution for Cranial Implant Design.