Accurate retinal vessel segmentation is a critical prerequisite for quantitative analysis of retinal images and computer-aided diagnosis of vascular diseases such as diabetic retinopathy. However, the elongated morphology, wide scale variation, and low contrast of retinal vessels pose significant challenges for existing methods, making it difficult to simultaneously preserve fine capillaries and maintain global topological continuity. To address these challenges, we propose the Vessel-aware Frequency-domain and Global Spatial modeling Network (VFGS-Net), an end-to-end segmentation framework that seamlessly integrates frequency-aware feature enhancement, dual-path convolutional representation learning, and bidirectional asymmetric spatial state-space modeling within a unified architecture. Specifically, VFGS-Net employs a dual-path feature convolution module to jointly capture fine-grained local textures and multi-scale contextual semantics. A novel vessel-aware frequency-domain channel attention mechanism is introduced to adaptively reweight spectral components, thereby enhancing vessel-relevant responses in high-level features. Furthermore, at the network bottleneck, we propose a bidirectional asymmetric Mamba2-based spatial modeling block to efficiently capture long-range spatial dependencies and strengthen the global continuity of vascular structures. Extensive experiments on four publicly available retinal vessel datasets demonstrate that VFGS-Net achieves competitive or superior performance compared to state-of-the-art methods. Notably, our model consistently improves segmentation accuracy for fine vessels, complex branching patterns, and low-contrast regions, highlighting its robustness and clinical potential.

翻译：精确的视网膜血管分割是进行视网膜图像定量分析以及糖尿病视网膜病变等血管疾病计算机辅助诊断的关键前提。然而，视网膜血管的细长形态、大尺度变化以及低对比度特性给现有方法带来了重大挑战，使得同时保留细微毛细血管和维持全局拓扑连续性变得困难。为应对这些挑战，我们提出了血管感知频域与全局空间建模网络（VFGS-Net），这是一个端到端的分割框架，它将频率感知特征增强、双路径卷积表示学习以及双向非对称空间状态空间建模无缝集成在一个统一的架构中。具体而言，VFGS-Net采用双路径特征卷积模块来联合捕获细粒度的局部纹理和多尺度上下文语义。我们引入了一种新颖的血管感知频域通道注意力机制，以自适应地重新加权频谱分量，从而增强高层特征中与血管相关的响应。此外，在网络瓶颈处，我们提出了一个基于双向非对称Mamba2的空间建模块，以高效捕获长程空间依赖性并加强血管结构的全局连续性。在四个公开可用的视网膜血管数据集上进行的大量实验表明，与最先进的方法相比，VFGS-Net取得了具有竞争力或更优的性能。值得注意的是，我们的模型在细微血管、复杂分支模式和低对比度区域的细分精度上持续提升，凸显了其鲁棒性和临床潜力。