Rotterdam artery-vein segmentation (RAV) dataset

TL;DR

This paper introduces the Rotterdam artery-vein segmentation (RAV) dataset, a comprehensive collection of annotated fundus images designed to advance machine learning models for retinal vascular analysis in ophthalmology.

Contribution

The dataset provides high-quality, diverse, and connectivity-verified artery-vein annotations on a large set of fundus images, supporting robust model training and benchmarking.

Findings

Includes 1024x1024 images with multiple modalities

Contains challenging images with variable quality

Enables development of generalizable vascular analysis tools

Abstract

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.