{"ID":2848870,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.24009","arxiv_id":"2510.24009","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","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.","short_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,...","url_abs":"https://arxiv.org/abs/2510.24009","url_pdf":"https://arxiv.org/pdf/2510.24009v1","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\"]","published":"2025-10-28T02:33:45Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}