Precise segmentation of retinal arteries and veins carries the diagnosis of systemic cardiovascular conditions. However, standard convolutional architectures often yield topologically disjointed segmentations, characterized by gaps and discontinuities that render reliable graph-based clinical analysis impossible despite high pixel-level accuracy. To address this, we introduce a topology-aware framework engineered to maintain vascular connectivity. Our architecture fuses a Topological Feature Fusion Module (TFFM) that maps local feature representations into a latent graph space, deploying Graph Attention Networks to capture global structural dependencies often missed by fixed receptive fields. Furthermore, we drive the learning process with a hybrid objective function, coupling Tversky loss for class imbalance with soft clDice loss to explicitly penalize topological disconnects. Evaluation on the Fundus-AVSeg dataset reveals state-of-the-art performance, achieving a combined Dice score of 90.97% and a 95% Hausdorff Distance of 3.50 pixels. Notably, our method decreases vessel fragmentation by approximately 38% relative to baselines, yielding topologically coherent vascular trees viable for automated biomarker quantification. We open-source our code at https://tffm-module.github.io/.

翻译：视网膜动脉与静脉的精确分割对系统性心血管疾病的诊断至关重要。然而，标准卷积架构常产生拓扑结构断裂的分割结果，表现为血管间隙与不连续性，尽管像素级精度较高，却导致无法进行可靠的基于图结构的临床分析。为解决此问题，我们提出一种专为保持血管连通性设计的拓扑感知框架。该架构融合了拓扑特征融合模块（TFFM），将局部特征表示映射至潜在图空间，并利用图注意力网络捕获固定感受野常忽略的全局结构依赖关系。此外，我们采用混合目标函数驱动学习过程，结合处理类别不平衡的Tversky损失与显式惩罚拓扑断裂的软性clDice损失。在Fundus-AVSeg数据集上的评估表明，本方法达到最先进性能：综合Dice分数为90.97%，95%豪斯多夫距离为3.50像素。值得注意的是，相较于基线方法，本方法将血管断裂率降低约38%，生成可用于自动化生物标志物量化的拓扑连贯血管树。代码已开源：https://tffm-module.github.io/。