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Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition

First Challenge, SEG.A. 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

  • Conference proceedings
  • © 2024

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Editors:
  1. Antonio Pepe

    1. Graz University of Technology, Graz, Austria

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  2. Gian Marco Melito

    1. Graz University of Technology, Graz, Austria

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  3. Jan Egger

    1. Essen University Hospital, Essen, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14539)

Included in the following conference series:

Conference proceedings info: SEGA 2023.

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About this book

This book constitutes the First Segmentation of the Aorta Challenge, SEG.A. 2023, which was held in conjunction with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, on October 8, 2023. 
The 8 full and 3 short papers presented have been carefully reviewed and selected for inclusion in the book. They focus specifically on robustness, visual quality and meshing of automatically generated segmentations of aortic vessel trees from CT imaging. The challenge was organized as a ”container submission” challenge, where participants had to upload their algorithms to Grand Challenge in the form of Docker containers. Three tasks were created for SEG.A. 2023.






Keywords

Table of contents (11 papers)

  1. Front Matter

    Pages i-xii
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  2. M3F: Multi-Field-of-View Feature Fusion Network for Aortic Vessel Tree Segmentation in CT Angiography

    • Yunsu Byeon, Hyeseong Kim, Kyungwon Kim, Doohyun Park, Euijoon Choi, Dosik Hwang
    Pages 1-12

  3. Aorta Segmentation from 3D CT in MICCAI SEG.A. 2023 Challenge

    • Andriy Myronenko, Dong Yang, Yufan He, Daguang Xu
    Pages 13-18

  4. A Data-Centric Approach for Segmenting the Aortic Vessel Tree: A Solution to SEG.A. Challenge 2023 Segmentation Task

    • Ayman El-Ghotni, Mohamed Nabil, Hossam El-Kady, Ahmed Ayyad, Amr Nasr
    Pages 19-41

  5. Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge

    • Marek Wodzinski, Henning Müller
    Pages 42-54

  6. Position-Encoded Pixel-to-Prototype Contrastive Learning for Aortic Vessel Tree Segmentation

    • Hyeongyu Kim, Yejee Shin, Dosik Hwang
    Pages 55-66

  7. Misclassification Loss for Segmentation of the Aortic Vessel Tree

    • Abbas Khan, Muhammad Asad, Alexander Zolotarev, Caroline Roney, Anthony Mathur, Martin Benning et al.
    Pages 67-79

  8. Deep Learning-Based Segmentation and Mesh Reconstruction of the Aortic Vessel Tree from CTA Images

    • Theodoros Panagiotis Vagenas, Konstantinos Georgas, George K. Matsopoulos
    Pages 80-94

  9. RASNet: U-Net-Based Robust Aortic Segmentation Network for Multicenter Datasets

    • Jihan Zhang, Zhen Zhang, Liqin Huang
    Pages 95-109

  10. Optimizing Aortic Segmentation with an Innovative Quality Assessment: The Role of Global Sensitivity Analysis

    • Gian Marco Melito, Antonio Pepe, Alireza Jafarinia, Thomas Krispel, Jan Egger
    Pages 110-126

  11. A Mini Guide on Mesh Generation of Blood Vessels for CFD Applications

    • Domagoj Bošnjak, Thomas-Peter Fries
    Pages 127-134

  12. Aortic Segmentations and Their Possible Clinical Benefits

    • Christian Mayer, Melanie Arnreiter, Barbara Karner, Sophie Hossain, Hannes Deutschmann, Daniel Zimpfer et al.
    Pages 135-140

  13. Back Matter

    Pages 141-142
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Other volumes

  1. Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition

Editors and Affiliations

  • Graz University of Technology, Graz, Austria

    Antonio Pepe, Gian Marco Melito

  • Essen University Hospital, Essen, Germany

    Jan Egger

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