GA-TAN: a graph aggregation and topology-aware network for unified 3D ocular-cerebral vascular segmentation.

Background: The eye and brain share developmental and regulatory vascular similarities, and 3D vascular segmentation from Optical Coherence Tomography Angiography (OCTA) and Magnetic Resonance Angiography (MRA) is essential for neuro-ophthalmic assessment. However, vascular trees exhibit extreme multi-scale morphology and are vulnerable to topology disruptions caused by OCTA artifacts (e.g., noise and discontinuities) and MRA anatomical variants (e.g., Circle of Willis incompleteness), which often lead to fragmented vessels and unstable segment-wise predictions. Purpose: To develop a unified 3D vascular segmentation framework that transfers and shares structural priors across OCTA and MRA, enabling accurate parsing under extreme scale imbalance and topology disruptions, and producing anatomically plausible predictions that remain robust to imaging artifacts in OCTA and Circle-of-Willis variants in MRA. Methods: We propose GA-TAN, a Graph Aggregation and Topology-Aware Network for unified ocular-cerebral vascular segmentation. GA-TAN employs a variable-window hybrid CNN-Transformer backbone to adapt receptive fields across vessel scales, an Efficient Multi-Scale Attention (EMA) module to enhance volumetric spatial-channel interactions, and a Multi-Scale Graph Aggregation (MSGA) module to perform global topological reasoning and hierarchical feature fusion. A topology-aware training objective further supervises segment existence and connectivity to penalize structural discontinuities beyond pixel-wise losses. Experiments were conducted on Retina3D for binary 3D OCTA vessel segmentation and on TopCow for 13-class CoW artery segmentation from MRA. Results: GA-TAN achieved the best overall performance on Retina3D, with a DSC of 0.684 and a clDice of 0.667, indicating improved overlap accuracy and centerline connectivity preservation. On TopCow, GA-TAN obtained a mean Dice of 0.814 across 13 arterial categories and produced more anatomically consistent segment-wise predictions, showing clear advantages on thin communicating and variant-related segments. Ablation studies further validated the complementary contributions of EMA, MSGA, and topology-aware supervision. Conclusion: GA-TAN provides a unified and topology-aware solution for 3D vascular segmentation across OCTA and MRA. By integrating adaptive multi-scale modeling, graph-based global aggregation, and explicit topology supervision, the proposed framework improves both segmentation accuracy and vascular ontinuity, enhancing robustness to imaging artifacts and anatomical variants for ocular-cerebral vascular analysis.