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.

翻译：我们提出CaravelMetrics，一种用于自动化脑血管分析的计算框架，该框架通过骨架化导出的图表示来建模血管形态。该框架整合了基于图谱的区域分割、中心线提取和图构建，以计算十五种形态计量学、拓扑学、分形和几何特征。这些特征可以从完整的血管网络全局估计，也可以在动脉区域内局部估计，从而实现对脑血管组织的多尺度表征。应用于来自IXI数据集的570个3D TOF-MRA扫描（年龄20-86岁），CaravelMetrics生成了可重复的血管图，捕捉到了与年龄和性别相关的变异，以及与教育水平相关的血管复杂性增加，这与文献中报道的发现一致。该框架为定量脑血管特征提取提供了一种可扩展且全自动的方法，支持血管健康与衰老的规范建模和群体水平研究。