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A collection of the papers/codes and datasets associated with the MICCAI 2020 Cranial Implant Design Challenge.

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Event Venue Organizers Website
AutoImplant 2020 MICCAI 2020 Jianning Li and Jan Egger et al. https://autoimplant.grand-challenge.org/
Authors Venue Title BibTex
Li, J., Pimentel, P., Szengel, A., Ehlke, M., Lamecker, H., et al IEEE TMI AutoImplant 2020 - First MICCAI Challenge on Automatic Cranial Implant Design BibTex
Proceedings Editors Publisher Springer Link Citation Google Book
Towards the Automatization of Cranial Implant Design in Cranioplasty Jianning Li and Jan Egger Springer International Publishing Springer Link BibTex Google Book
title code materials cite
A baseline approach for autoimplant: the miccai 2020 cranial implant design challenge github n/a BibTex
Patient Specific Implants (PSI): Cranioplasty in the Neurosurgical Clinical Routine n/a n/a BibTex
Dataset Descriptor for the AutoImplant Cranial Implant Design Challenge n/a n/a BibTex
Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks n/a n/a BibTex
Cranial Implant Design Through Multiaxial Slice Inpainting Using Deep Learning github n/a BibTex
Cranial Implant Design via Virtual Craniectomy with Shape Priors github A3(s), A3 BibTex
Deep Learning Using Augmentation via Registration: 1st Place Solution to the AutoImplant 2020 Challenge github dataset BibTex
Cranial Defect Reconstruction using Cascaded CNN with Alignment github n/a BibTex
Shape Completion by U-Net: An Approach to the AutoImplant MICCAI Cranial Implant Design Challenge n/a n/a BibTex
Cranial Implant Prediction using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement github n/a BibTex
Cranial Implant Design Using a Deep Learning Method with Anatomical Regularization n/a n/a BibTex
High-Resolution Cranial Implant Prediction via Patch-Wise Training github n/a BibTex
Learning Volumetric Shape Super-Resolution for Cranial Implant Design github n/a BibTex

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A collection of the papers/codes and datasets associated with the MICCAI 2020 Cranial Implant Design Challenge.

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