🤖 AI Summary
High-quality, diverse annotations for arteriole–venule (A/V) segmentation in fundus images are scarce, limiting model generalizability and clinical reliability. Method: We introduce the first publicly available, high-resolution (1024×1024) color fundus image dataset, capturing real-world clinical variability across imaging devices, age groups, and acquisition conditions. We propose an explicit connectivity-validated hierarchical A/V annotation paradigm—enabling, for the first time in a public fundus dataset, topology-aware vascular consistency verification. Low-quality images discarded by conventional quality control—but retaining clinical relevance—are intentionally retained to enhance robustness. We further develop a customized multi-level annotation interface, a connected-component–based visualization tool for manual correction, and a multimodal preprocessing pipeline (including raw images, contrast-enhanced images, and RGB-encoded connectivity-validation masks). Contribution/Results: This dataset significantly improves the robustness and clinical applicability of vascular analysis models in real-world deployment scenarios.
📝 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.