构建多中心主动脉树分割数据集，推动自动化分析发展
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
- 采用3D U-Net深度学习架构，融合多个顶尖模型提升性能
- 集成模型显著优于单个模型，验证了模型融合的有效性
- 提供公开数据集与基准，助力临床可转化工具研发
从计算机断层血管造影（CTA）中自动分析主动脉血管树（AVT）具有巨大临床潜力，但受限于缺乏共享的高质量数据。我们发起了SEG.A.挑战赛，引入一个大规模、公开可用的多机构数据集，用于主动脉树分割。该挑战在隐藏测试集上评估了自动化算法，并随后增加了面向计算模拟的表面网格生成任务。研究发现，深度学习方法明显占优，3D U-Net架构主导了排名靠前的提交方案。关键结果表明，最优算法的集成模型显著优于单个模型，凸显了模型融合的优势。性能与算法设计密切相关，尤其是定制化的后处理步骤及训练数据特性。该计划不仅确立了新的性能基准，还为未来开发鲁棒、临床可应用的工具提供了持久资源。
原文摘要 · Abstract (English)
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.
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