Towards the Automatic Segmentation, Modeling and Meshing of the Aortic Vessel Tree from Multicenter Acquisitions: An Overview of the SEG.A. 2023 Segmentation of the Aorta Challenge

• URL: http://arxiv.org/abs/2510.24009v1
• Date: Tue, 28 Oct 2025 02:33:45 GMT
• Title: Towards the Automatic Segmentation, Modeling and Meshing of the Aortic Vessel Tree from Multicenter Acquisitions: An Overview of the SEG.A. 2023 Segmentation of the Aorta Challenge
• Authors: Yuan Jin, Antonio Pepe, Gian Marco Melito, Yuxuan Chen, Yunsu Byeon, Hyeseong Kim, Kyungwon Kim, Doohyun Park, Euijoon Choi, Dosik Hwang, Andriy Myronenko, Dong Yang, Yufan He, Daguang Xu, Ayman El-Ghotni, Mohamed Nabil, Hossam El-Kady, Ahmed Ayyad, Amr Nasr, Marek Wodzinski, Henning Müller, Hyeongyu Kim, Yejee Shin, Abbas Khan, Muhammad Asad, Alexander Zolotarev, Caroline Roney, Anthony Mathur, Martin Benning, Gregory Slabaugh, Theodoros Panagiotis Vagenas, Konstantinos Georgas, George K. Matsopoulos, Jihan Zhang, Zhen Zhang, Liqin Huang, Christian Mayer, Heinrich Mächler, Jan Egger,
• Abstract summary: We introduce the SEG.A. challenge to catalyze progress in this field.<n>The challenge benchmarked automated algorithms on a hidden test set.<n>A key result was that an ensemble of the highest-ranking algorithms significantly outperformed individual models.
• Score: 44.01488489205175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The automated analysis of the aortic vessel tree (AVT) from computed tomography angiography (CTA) holds immense clinical potential, but its development has been impeded by a lack of shared, high-quality data. We launched the SEG.A. challenge to catalyze progress in this field by introducing a large, publicly available, multi-institutional dataset for AVT segmentation. The challenge benchmarked automated algorithms on a hidden test set, with subsequent optional tasks in surface meshing for computational simulations. Our findings reveal a clear convergence on deep learning methodologies, with 3D U-Net architectures dominating the top submissions. A key result was that an ensemble of the highest-ranking algorithms significantly outperformed individual models, highlighting the benefits of model fusion. Performance was strongly linked to algorithmic design, particularly the use of customized post-processing steps, and the characteristics of the training data. This initiative not only establishes a new performance benchmark but also provides a lasting resource to drive future innovation toward robust, clinically translatable tools.

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