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Towards the Automatization of Cranial Implant Design in Cranioplasty II

Second Challenge, AutoImplant 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Jianning Li

    1. Graz University of Technology, Graz, Austria

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    You can also search for this editor in PubMed   Google Scholar

  2. Jan Egger

    1. Graz University of Technology, Graz, Austria

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13123)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: AutoImplant 2021.

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Table of contents (10 papers)

  1. Front Matter

    Pages i-ix
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  2. Personalized Calvarial Reconstruction in Neurosurgery

    • Laurèl Rauschenbach, Christoph Rieß, Ulrich Sure, Karsten H. Wrede
    Pages 1-7

  3. Qualitative Criteria for Feasible Cranial Implant Designs

    • David G. Ellis, Carlos M. Alvarez, Michele R. Aizenberg
    Pages 8-18

  4. Segmentation of Defective Skulls from CT Data for Tissue Modelling

    • OldÅ™ich Kodym, Michal Å panÄ›l, Adam Herout
    Pages 19-28

  5. Improving the Automatic Cranial Implant Design in Cranioplasty by Linking Different Datasets

    • Marek Wodzinski, Mateusz Daniol, Daria Hemmerling
    Pages 29-44

  6. Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation

    • Jianning Li, Antonio Pepe, Christina Gsaxner, Yuan Jin, Jan Egger
    Pages 45-62

  7. A U-Net Based System for Cranial Implant Design with Pre-processing and Learned Implant Filtering

    • Hamza Mahdi, Allison Clement, Evan Kim, Zachary Fishman, Cari M. Whyne, James G. Mainprize et al.
    Pages 63-79

  8. Sparse Convolutional Neural Network for Skull Reconstruction

    • Artem Kroviakov, Jianning Li, Jan Egger
    Pages 80-94

  9. Cranial Implant Prediction by Learning an Ensemble of Slice-Based Skull Completion Networks

    • Bokai Yang, Ke Fang, Xingyu Li
    Pages 95-104

  10. PCA-Skull: 3D Skull Shape Modelling Using Principal Component Analysis

    • Lei Yu, Jianning Li, Jan Egger
    Pages 105-115

  11. Cranial Implant Design Using V-Net Based Region of Interest Reconstruction

    • Shashwat Pathak, Chitimireddy Sindhura, Rama Krishna Sai S. Gorthi, Degala Venkata Kiran, Subrahmanyam Gorthi
    Pages 116-128

  12. Back Matter

    Pages 129-129
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  1. Towards the Automatization of Cranial Implant Design in Cranioplasty II

Keywords

About this book

This book constitutes the Second Automatization of Cranial Implant Design in Cranioplasty Challenge, AutoImplant 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, in Strasbourg, France, in September, 2021. The challenge took place virtually due to the COVID-19 pandemic.

The 7 papers are presented together with one invited paper, one qualitative evaluation criteria from neurosurgeons and a dataset descriptor. 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.

Editors and Affiliations

  • Graz University of Technology, Graz, Austria

    Jianning Li, Jan Egger

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