Generalized Retinal Artery/Vein Segmentation via Multi-Dataset Fine-Tuning and Pathology Subgroup Analysis.

backgroundAutomatic artery/vein (A/V) segmentation in color fundus photography underpins retinal biomarkers such as the arteriolar-to-venular ratio (AVR), yet single-dataset models generalize poorly across institutions and pathologies.

methodsUsing pre-trained Recursive Refinement W-Net (RRWNet) weights as initialization, eight heterogeneous datasets were jointly fine-tuned under a single set of weights for 50 epochs: five A/V-labeled (Fundus-AVSeg, LES-AV, RITE, FIREFLY-Gen, PSEUDO_AV) and three vessel-only (DRIVE, CHASE_DB1, HRF). A vessel-only loss separation strategy applied only the vessel-channel loss to vessel-only datasets, preventing A/V metric dilution; post-processing combined field-of-view masking, connected-component denoising, and morphological refinement, outputting artery, vein, and vessel masks with crossing and junction maps.

resultsLoss separation recovered validation DSC from 0.518 to 0.570 (AUC up to 0.959). On re-inference across four datasets (332 images), the single-weight model attained mean-AV DSC 0.657 ± 0.085 and AUC 0.976 ± 0.016; across normal, AMD, glaucoma, and DR subgroups, performance was statistically indistinguishable (Kruskal-Wallis, all

conclusionsWithin a leakage-aware internal evaluation, the single-weight model produced stable A/V segmentation across the four datasets and across pathology subgroups, indicating preliminary internal consistency rather than proven cross-domain generalization. Because the evaluation reuses data seen during training and no external or patient-level held-out set was used, robustness claims are deferred; leakage-free external validation and patient-level re-evaluation are identified as the essential next steps.