retinalysis-vascx: An explainable software toolbox for the extraction of retinal vascular biomarkers
This provides a tool for researchers and clinicians in ophthalmology and epidemiology to standardize biomarker extraction, though it is incremental as it builds on existing segmentation methods.
The authors tackled the problem of automatically extracting retinal vascular biomarkers from color fundus images by developing VascX, an open-source Python toolbox that processes vessel segmentations into graphs to compute biomarkers like vascular density and tortuosity, resulting in a computationally efficient solution for large-scale studies.
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.