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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2510.24009 |
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| _version_ | 1866914117800951808 |
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| author | Jin, Yuan Pepe, Antonio Melito, Gian Marco Chen, Yuxuan Byeon, Yunsu Kim, Hyeseong Kim, Kyungwon Park, Doohyun Choi, Euijoon Hwang, Dosik Myronenko, Andriy Yang, Dong He, Yufan Xu, Daguang El-Ghotni, Ayman Nabil, Mohamed El-Kady, Hossam Ayyad, Ahmed Nasr, Amr Wodzinski, Marek Müller, Henning Kim, Hyeongyu Shin, Yejee Khan, Abbas Asad, Muhammad Zolotarev, Alexander Roney, Caroline Mathur, Anthony Benning, Martin Slabaugh, Gregory Vagenas, Theodoros Panagiotis Georgas, Konstantinos Matsopoulos, George K. Zhang, Jihan Zhang, Zhen Huang, Liqin Mayer, Christian Mächler, Heinrich Egger, Jan |
| author_facet | Jin, Yuan Pepe, Antonio Melito, Gian Marco Chen, Yuxuan Byeon, Yunsu Kim, Hyeseong Kim, Kyungwon Park, Doohyun Choi, Euijoon Hwang, Dosik Myronenko, Andriy Yang, Dong He, Yufan Xu, Daguang El-Ghotni, Ayman Nabil, Mohamed El-Kady, Hossam Ayyad, Ahmed Nasr, Amr Wodzinski, Marek Müller, Henning Kim, Hyeongyu Shin, Yejee Khan, Abbas Asad, Muhammad Zolotarev, Alexander Roney, Caroline Mathur, Anthony Benning, Martin Slabaugh, Gregory Vagenas, Theodoros Panagiotis Georgas, Konstantinos Matsopoulos, George K. Zhang, Jihan Zhang, Zhen Huang, Liqin Mayer, Christian Mächler, Heinrich Egger, Jan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24009 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 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 Jin, Yuan Pepe, Antonio Melito, Gian Marco Chen, Yuxuan Byeon, Yunsu Kim, Hyeseong Kim, Kyungwon Park, Doohyun Choi, Euijoon Hwang, Dosik Myronenko, Andriy Yang, Dong He, Yufan Xu, Daguang El-Ghotni, Ayman Nabil, Mohamed El-Kady, Hossam Ayyad, Ahmed Nasr, Amr Wodzinski, Marek Müller, Henning Kim, Hyeongyu Shin, Yejee Khan, Abbas Asad, Muhammad Zolotarev, Alexander Roney, Caroline Mathur, Anthony Benning, Martin Slabaugh, Gregory Vagenas, Theodoros Panagiotis Georgas, Konstantinos Matsopoulos, George K. Zhang, Jihan Zhang, Zhen Huang, Liqin Mayer, Christian Mächler, Heinrich Egger, Jan Computer Vision and Pattern Recognition 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. |
| 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 |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.24009 |