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Jose D. Vargas Quiros, Michael J. Beyeler, Sofia Ortin Vela et al.

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.

Jose Vargas Quiros, Bart Liefers, Karin van Garderen et al.

We introduce VascX models, a comprehensive set of model ensembles for analyzing retinal vasculature from color fundus images (CFIs). Annotated CFIs were aggregated from public datasets . Additional CFIs, mainly from the population-based Rotterdam Study were annotated by graders for arteries and veins at pixel level, resulting in a dataset diverse in patient demographics and imaging conditions. VascX models demonstrated superior segmentation performance across datasets, image quality levels, and anatomic regions when compared to existing, publicly available models, likely due to the increased size and variety of our training set. Important improvements were observed in artery-vein and disc segmentation performance, particularly in segmentations of these structures on CFIs of intermediate quality, common in large cohorts and clinical datasets. Importantly, these improvements translated into significantly more accurate vascular features when we compared features extracted from VascX segmentation masks with features extracted from segmentation masks generated by previous models. With VascX models we provide a robust, ready-to-use set of model ensembles and inference code aimed at simplifying the implementation and enhancing the quality of automated retinal vasculature analyses. The precise vessel parameters generated by the model can serve as starting points for the identification of disease patterns in and outside of the eye.

Jose Vargas Quiros, Bart Liefers, Karin van Garderen et al.

Purpose: To provide a diverse, high-quality dataset of color fundus images (CFIs) with detailed artery-vein (A/V) segmentation annotations, supporting the development and evaluation of machine learning algorithms for vascular analysis in ophthalmology. Methods: CFIs were sampled from the longitudinal Rotterdam Study (RS), encompassing a wide range of ages, devices, and capture conditions. Images were annotated using a custom interface that allowed graders to label arteries, veins, and unknown vessels on separate layers, starting from an initial vessel segmentation mask. Connectivity was explicitly verified and corrected using connected component visualization tools. Results: The dataset includes 1024x1024-pixel PNG images in three modalities: original RGB fundus images, contrast-enhanced versions, and RGB-encoded A/V masks. Image quality varied widely, including challenging samples typically excluded by automated quality assessment systems, but judged to contain valuable vascular information. Conclusion: This dataset offers a rich and heterogeneous source of CFIs with high-quality segmentations. It supports robust benchmarking and training of machine learning models under real-world variability in image quality and acquisition settings. Translational Relevance: By including connectivity-validated A/V masks and diverse image conditions, this dataset enables the development of clinically applicable, generalizable machine learning tools for retinal vascular analysis, potentially improving automated screening and diagnosis of systemic and ocular diseases.