自动化分析脑血管结构，支持大规模人群研究。
An Automated Framework for Large-Scale Graph-Based Cerebrovascular Analysis
- 通过骨架化构建图模型，自动提取血管形态特征。
- 在570例扫描中验证，发现年龄、性别和教育程度影响血管复杂度。
- 适合神经影像与老龄化研究者使用。
我们提出CaravelMetrics，一个用于自动化脑血管分析的计算框架，通过骨架化衍生的图表示建模血管形态。该框架整合基于图谱的区域分割、中心线提取与图结构构建，计算十五项形态学、拓扑学、分形与几何特征。这些特征可全局分析完整血管网络或局部在动脉供血区，实现多尺度脑血管组织表征。应用于IXI数据集中的570例3D TOF-MRA扫描（年龄20-86岁），结果生成可重复的血管图，揭示年龄、性别相关变化及教育水平升高带来的血管复杂度提升，与已有文献一致。该框架提供可扩展、全自动的定量脑血管特征提取方法，支持正常值建模与血管健康及衰老的群体研究。
原文摘要 · Abstract (English)
We present CaravelMetrics, a computational framework for automated cerebrovascular analysis that models vessel morphology through skeletonization-derived graph representations. The framework integrates atlas-based regional parcellation, centerline extraction, and graph construction to compute fifteen morphometric, topological, fractal, and geometric features. The features can be estimated globally from the complete vascular network or regionally within arterial territories, enabling multiscale characterization of cerebrovascular organization. Applied to 570 3D TOF-MRA scans from the IXI dataset (ages 20-86), CaravelMetrics yields reproducible vessel graphs capturing age- and sex-related variations and education-associated increases in vascular complexity, consistent with findings reported in the literature. The framework provides a scalable and fully automated approach for quantitative cerebrovascular feature extraction, supporting normative modeling and population-level studies of vascular health and aging.
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