retinalysis-vascx: An explainable software toolbox for the extraction of retinal vascular biomarkers

2026-02-09

AI Summary

Current methods lack scalable, standardized, and interpretable tools for the automatic extraction of retinal vascular biomarkers from color fundus photographs. To address this gap, this work proposes VascX, an open-source Python toolbox that constructs both directed and undirected vascular graphs from vessel segmentation masks, incorporates anatomical landmarks such as the macula and optic disc to enable spatial standardization, and introduces a region-aware mechanism to facilitate the automatic identification of non-computable biomarkers. The framework integrates vessel skeletonization, graph-based modeling, grid-based localization, and visualization techniques within a modular architecture to ensure interpretability and reproducibility. VascX efficiently supports the automated computation of diverse biomarkers—including vascular density, bifurcation angle, central retinal equivalent, tortuosity, and temporal angle—making it well-suited for large-scale clinical and epidemiological studies.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Web data visualization

Abstract

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.

Problem

• retinal vascular biomarkers
• color fundus images
• automated extraction
• oculomics
• vessel segmentation

Innovation

• retinal vascular biomarkers
• explainable software toolbox
• region-aware analysis
• vessel graph reconstruction
• oculomics