arXiv subjects

Eyened Reading Center

Publications and source records attributed to Eyened Reading Center.

3 recordsLinked to original sources

arXiv2026

Rotterdam artery-vein segmentation (RAV) dataset

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.

eess.IV↗

arXiv2025

retinalysis-fundusprep: A python package for robust color fundus image bounds extraction

Color fundus image (CFI) bounds detection and contrast enhancement are fundamental tasks in automatic CFI analysis. We present an open-source algorithm, published as a Python package, to automatically extract parametric bounds from color fundus images, and for applying contrast enhancement. The software has applications in automated biomarker calculation and AI systems. Bounds detection was implemented by detecting the CFI's contour via a shortest path algorithm on a polar transformation of the image. A second step detects points along the CFI circle robustly via a circle-fitting RANSAC algorithm. Straight boundaries are detected independently. Finally, the CFI is mirrored along its bounds before contrast enhancement to eliminate edge artifacts. We manually evaluated correctness on the EyeQ and Rotterdam Study datasets. Evaluation on the EyeQ CFI quality dataset revealed an error rate of 0.2% for our method, compared to 1.9% for previous work. On a sample of challenging CFI images from the Rotterdam Study, our method did not produce any mistakes. retinalysis-fundusprep is an improvement over existing publicly available code for CFI bounds detection. The improved reliability may allow for its use on a wider range of datasets without the need for manual validation.

q-bio.OT↗

arXiv2024

VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images

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.

eess.IV↗