The automatic extraction of retinal vascular biomarkers from color fundus images (CFI) is essential for large-scale studies of the retinal vasculature. We present VascX, an open-source Python toolbox designed for the automated extraction of biomarkers from artery and vein segmentations. The VascX workflow processes vessel segmentation masks into skeletons to build undirected and directed vessel graphs, which are then used to resolve segments into continuous vessels. This architecture enables the calculation of a comprehensive suite of biomarkers, including vascular density, bifurcation angles, central retinal equivalents (CREs), tortuosity, and temporal angles, alongside image quality metrics. A distinguishing feature of VascX is its region awareness; by utilizing the fovea, optic disc, and CFI boundaries as anatomical landmarks, the tool ensures spatially standardized measurements and identifies when specific biomarkers are not computable. Spatially localized biomarkers are calculated over grids relative to these landmarks, facilitating precise clinical analysis. Released via GitHub and PyPI, VascX provides an explainable and modifiable framework that supports reproducible vascular research through integrated visualizations. By enabling the rapid extraction of established biomarkers and the development of new ones, VascX advances the field of oculomics, offering a robust, computationally efficient solution for scalable deployment in large-scale clinical and epidemiological databases.

翻译：从彩色眼底图像中自动提取视网膜血管生物标志物对于大规模视网膜血管研究至关重要。本文介绍VascX——一个专为从动静脉分割结果中自动提取生物标志物而设计的开源Python工具箱。VascX工作流程将血管分割掩码处理为骨架图，构建无向及有向血管网络，进而将离散片段解析为连续血管。该架构支持计算包括血管密度、分叉角度、视网膜中央等效参数、迂曲度与时相角在内的综合生物标志物指标，同时提供图像质量评估。VascX的突出特性在于其区域感知能力：通过将黄斑中心凹、视盘及眼底图像边界作为解剖标志，该工具确保空间标准化测量，并能识别特定生物标志物不可计算的情况。空间局部化生物标志物基于这些标志物构建的相对网格进行计算，为精准临床分析提供支持。通过GitHub和PyPI发布的VascX提供可解释、可修改的框架，其集成的可视化功能保障了血管研究的可重复性。通过实现成熟生物标志物的快速提取及新指标的开发，VascX推动了眼组学领域发展，为大规模临床与流行病学数据库的可扩展部署提供了稳健高效的计算解决方案。