Towards the Automatization of Cranial Implant Design in Cranioplasty

1. Front Matter

   Pages i-xvi

   PDF

2. Patient Specific Implants (PSI)

   • Gord von Campe, Karin Pistracher

   Pages 1-9

3. Dataset Descriptor for the AutoImplant Cranial Implant Design Challenge

   • Jianning Li, Jan Egger

   Pages 10-15

4. Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks

   • Pedro Pimentel, Angelika Szengel, Moritz Ehlke, Hans Lamecker, Stefan Zachow, Laura Estacio et al.

   Pages 16-27

5. Cranial Implant Design Through Multiaxial Slice Inpainting Using Deep Learning

   • Haochen Shi, Xiaojun Chen

   Pages 28-36

6. Cranial Implant Design via Virtual Craniectomy with Shape Priors

   • Franco Matzkin, Virginia Newcombe, Ben Glocker, Enzo Ferrante

   Pages 37-46

7. Deep Learning Using Augmentation via Registration: 1st Place Solution to the AutoImplant 2020 Challenge

   • David G. Ellis, Michele R. Aizenberg

   Pages 47-55

8. Cranial Defect Reconstruction Using Cascaded CNN with Alignment

   • Oldřich Kodym, Michal Španěl, Adam Herout

   Pages 56-64

9. Shape Completion by U-Net: An Approach to the AutoImplant MICCAI Cranial Implant Design Challenge

   • James G. Mainprize, Zachary Fishman, Michael R. Hardisty

   Pages 65-76

10. Cranial Implant Prediction Using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement

    • Amirhossein Bayat, Suprosanna Shit, Adrian Kilian, Jürgen T. Liechtenstein, Jan S. Kirschke, Bjoern H. Menze

    Pages 77-84

11. Cranial Implant Design Using a Deep Learning Method with Anatomical Regularization

    • Bomin Wang, Zhi Liu, Yujun Li, Xiaoyan Xiao, Ranran Zhang, Yankun Cao et al.

    Pages 85-93

12. High-Resolution Cranial Implant Prediction via Patch-Wise Training

    • Yuan Jin, Jianning Li, Jan Egger

    Pages 94-103

13. Learning Volumetric Shape Super-Resolution for Cranial Implant Design

    • Matthias Eder, Jianning Li, Jan Egger

    Pages 104-113

14. Back Matter

    Pages 115-115

    PDF

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.

• artificial intelligence
• computer vision
• cranial implant design
• craniectomy
• cranioplasty
• craniotomy
• deep learning
• image analysis
• image processing
• image reconstruction
• machine learning
• neural networks
• reconstruction
• shape completion
• shape prior
• skull reconstruction
• statistical shape model

• Graz University of Technology, Graz, Austria

  Jianning Li, Jan Egger

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