Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed atthis https URL.

@article{imran2025_2502.05330,
  title={ Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge },
  author={ Muhammad Imran and Jonathan R. Krebs and Vishal Balaji Sivaraman and Teng Zhang and Amarjeet Kumar and Walker R. Ueland and Michael J. Fassler and Jinlong Huang and Xiao Sun and Lisheng Wang and Pengcheng Shi and Maximilian Rokuss and Michael Baumgartner and Yannick Kirchhof and Klaus H. Maier-Hein and Fabian Isensee and Shuolin Liu and Bing Han and Bong Thanh Nguyen and Dong-jin Shin and Park Ji-Woo and Mathew Choi and Kwang-Hyun Uhm and Sung-Jea Ko and Chanwoong Lee and Jaehee Chun and Jin Sung Kim and Minghui Zhang and Hanxiao Zhang and Xin You and Yun Gu and Zhaohong Pan and Xuan Liu and Xiaokun Liang and Markus Tiefenthaler and Enrique Almar-Munoz and Matthias Schwab and Mikhail Kotyushev and Rostislav Epifanov and Marek Wodzinski and Henning Muller and Abdul Qayyum and Moona Mazher and Steven A. Niederer and Zhiwei Wang and Kaixiang Yang and Jintao Ren and Stine Sofia Korreman and Yuchong Gao and Hongye Zeng and Haoyu Zheng and Rui Zheng and Jinghua Yue and Fugen Zhou and Bo Liu and Alexander Cosman and Muxuan Liang and Chang Zhao and Gilbert R. Upchurch Jr. and Jun Ma and Yuyin Zhou and Michol A. Cooper and Wei Shao },
  journal={arXiv preprint arXiv:2502.05330},
  year={ 2025 }
}