Rotterdam artery-vein segmentation (RAV) dataset

• URL: http://arxiv.org/abs/2512.17322v1
• Date: Fri, 19 Dec 2025 08:09:02 GMT
• Title: Rotterdam artery-vein segmentation (RAV) dataset
• Authors: Jose Vargas Quiros, Bart Liefers, Karin van Garderen, Jeroen Vermeulen, Eyened Reading Center, Caroline Klaver,
• Abstract summary: This dataset includes 1024x1024-pixel PNG images in three modalities: original RGB fundus images, contrast-enhanced versions, and RGB-encoded A/V masks.<n>It supports the development of machine learning algorithms for vascular analysis in ophthalmology.
• Score: 0.8004597666699035
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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