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

2025-10-27

AI Summary

This study addresses the need for automated aortic vascular tree (AVT) analysis in multicenter computed tomographic angiography (CTA) data. We introduce the first publicly available multicenter AVT segmentation challenge and release a large-scale, expert-annotated dataset. Methodologically, we employ a 3D U-Net–based deep learning framework augmented with a customized post-processing pipeline to achieve high-fidelity AVT segmentation and surface mesh generation. Our key contributions include: (i) empirical validation that model ensembling significantly improves generalizability across heterogeneous centers; (ii) systematic analysis revealing critical impacts of training data distribution characteristics and post-processing design on segmentation accuracy; and (iii) establishment of a new state-of-the-art benchmark on a held-out test set, where the ensemble model achieves Dice = 0.92. The dataset, code, and evaluation protocol provide a standardized, clinically translatable resource for vascular modeling, computational fluid dynamics simulation, and algorithmic assessment.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Ensemble Methods
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Automated aortic vessel tree segmentation from CT angiography
• Addressing lack of shared high-quality multi-institutional datasets
• Benchmarking algorithms for surface meshing in computational simulations

Innovation

• 3D U-Net architectures dominate aortic segmentation
• Ensemble of top algorithms outperforms individual models
• Customized post-processing steps enhance algorithmic performance