Automatic extraction of retinal vascular biomarkers from color fundus images (CFI) is crucial for large-scale studies of the retinal vasculature. We present VascX, an open-source Python toolbox that extracts biomarkers from CFI artery-vein segmentations. VascX starts from vessel segmentation masks, extracts their skeletons, builds undirected and directed vessel graphs, and resolves vessel segments into longer vessels. A comprehensive set of biomarkers is derived, including vascular density, central retinal equivalents (CREs), and tortuosity. Spatially localized biomarkers may be calculated over grids placed relative to the fovea and optic disc. VascX is released via GitHub and PyPI with comprehensive documentation and examples. Our test-retest reproducibility analysis on repeat imaging of the same eye by different devices shows that most VascX biomarkers have moderate to excellent agreement (ICC > 0.5), with important differences in the level of robustness of different biomarkers. Our analyses of biomarker sensitivity to image perturbations and heuristic parameter values support these differences and further characterize VascX biomarkers. Ultimately, VascX provides an explainable and easily modifiable feature-extraction toolbox that complements segmentation to produce reliable retinal vascular biomarkers. Our graph-based biomarker computation stages support reproducible, region-aware measurements suited for large-scale clinical and epidemiological research. By enabling easy extraction of existing biomarkers and rapid experimentation with new ones, VascX supports oculomics research. Its robustness and computational efficiency facilitate scalable deployment in large databases, while open-source distribution lowers barriers to adoption for ophthalmic researchers and clinicians.

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系统研究至关重要。我们提出VascX——一个开源Python工具箱，可从CFI动静脉分割结果中提取生物标志物。VascX以血管分割掩膜为起点，提取其骨架，构建有向和无向血管图，并将血管段解析为更长的血管。该工具可推导出包括血管密度、中央视网膜等效值和迂曲度在内的全面生物标志物集合。基于相对于中央凹和视盘放置的网格，可计算空间定位生物标志物。VascX通过GitHub和PyPI发布，附带完整文档和示例。针对不同设备对同一眼睛进行重复成像的测试-重测再现性分析表明，大多数VascX生物标志物具有中等至卓越的一致性（ICC>0.5），不同生物标志物的稳健性水平存在显著差异。我们对生物标志物对图像扰动和启发式参数值敏感性的分析支持这些差异，并进一步表征了VascX生物标志物。最终，VascX提供了一个可解释且易于修改的特征提取工具箱，可补充分割过程以产生可靠的视网膜血管生物标志物。我们基于图的生物标志物计算阶段支持适用于大规模临床和流行病学研究的可重复、区域感知测量。通过实现现有生物标志物的便捷提取和新生物标志物的快速实验，VascX支持眼组学研究。其稳健性和计算效率便于在大型数据库中实现可扩展部署，而开源发布降低了眼科研究人员和临床医生的采用门槛。