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Updated: Sep 21, 2026
Three-Dimensional Printing of a Complex Aortic Anomaly
Published on: November 1, 2018
Automatic segmentation and modeling of the aortic vessel tree: Overview of the SEG.A 2023 aorta segmentation
Yuan Jin1, Antonio Pepe2, Gian Marco Melito3
1Zhejiang Lab, Hangzhou, 311100, Zhejiang, China; Institute for Artificial Intelligence (AI) in Medicine (IKIM), University Medicine Essen (AöR), Girardetstr. 2, Essen, 45131, NRW, Germany; Institute of Computer Graphics and Vision (ICG), Graz University of Technology, Inffeldgasse 16/II, Graz, 8010, Styria, Austria.
Abstract:
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.