Multi-Granularity Topological Reasoning for Anatomically Consistent Vasculature Parsing

Quantitative analysis of retinal vascular morphology is vital for clinical decision-making and the investigation of systemic diseases. Central to this process is the accurate segmentation of retinal arteries and veins (A/V) from the background, a task challenged by substantial variations in vessel calibers and the presence of low-contrast or ambiguous structures in fundus images, especially in ultra-wide field imaging where peripheral distortions and large-scale anatomical variability are pronounced. These factors often lead to fragmented semantic representations and topological inconsistencies in automated segmentation outputs. To address these limitations, we propose Ultra, a multi-granularity topological reasoning network designed for precise A/V segmentation. Ultra adopts a cascaded two-stage architecture: PriorNet generates coarse, multi-scale vascular priors that provide structural guidance, while RefineNet performs topology-aware segmentation refinement. To further enforce topological coherence, we propose the neighboring pixel connectivity regularization (NICER) layer, which selectively integrates local connectivity information predicted by the proposed connectivity prediction union (CPU) module. This connectivity is employed as auxiliary supervision through a pixel-wise local connectivity loss, reinforcing structural reasoning and promoting anatomically consistent vascular topology inference. Extensive experiments on ultra-wide field fundus imaging (UWF) datasets demonstrate that Ultra achieves state-of-the-art performance in A/V segmentation and topological preservation. Moreover, Ultra generalizes well to conventional color fundus photography (CFP) datasets, underscoring its robustness and broad applicability. Code is publicly available at: https://github.com/iMED-Lab/Ultra