%Aigaion2 BibTeX export from HES SO Valais Publications
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@ARTICLE{,
    author = {M{\"{u}}ller, Henning and Wodzinski, Marek},
  keywords = {3D Segme, Aorta segmentation, AortaSeg24, Computed Tomography Angiography},
     title = {Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge},
   journal = {Arxiv},
      year = {2025},
       doi = {https://doi.org/10.48550/arXiv.2502.05330},
  abstract = {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.}
}