Automatic segmentation and modeling of the aortic vessel tree: Overview of the SEG.A 2023 aorta segmentation challenge.

The automated analysis of the aortic vessel tree (AVT) from computed tomography angiography (CTA) is crucial for clinical applications but lacks shared, high-quality data. To address this, we launched the SEG.A. challenge, introducing a large, public, multi-institutional dataset for AVT segmentation and benchmarking automated algorithms. The challenge results showed a strong trend toward deep learning, with 3D U-Net architectures being most effective. The winning solution used an ensemble-based strategy, highlighting the value of model ensembling for robust AVT segmentation. Performance strongly correlated with algorithmic design, notably the use of customized post-processing and training data characteristics. This initiative establishes a new performance benchmark and provides a lasting resource to drive future innovation toward robust, clinically translatable AVT analysis tools.