Abstract
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.
Abstract (translated)
精确的视网膜血管分割是定量分析视网膜图像和辅助诊断如糖尿病性视网膜病变等血管疾病的关键前提。然而，由于视网膜血管具有延长的形态、广泛的变化范围以及低对比度等特点，现有的方法在同时保持细小毛细血管并维持全局拓扑连续性方面面临着巨大挑战。为了解决这些问题，我们提出了一个基于频率域和全局空间建模的血管感知网络（VFGS-Net），这是一个端到端分割框架，它无缝集成了频率感知特征增强、双路径卷积表示学习以及双向不对称的空间状态空间模型。 具体来说，VFGS-Net采用了双重路径特征卷积模块来共同捕获细微局部纹理和多尺度上下文语义。此外，引入了一种新颖的血管感知频率域信道注意力机制，能够自适应地重新加权频谱成分，从而增强高层次特性中与血管相关的响应。 在模型瓶颈处，我们提出了一种基于双向不对称Mamba2的空间建模块，用以高效捕获长程空间依赖关系，并加强血管结构的全局连续性。我们在四个公开可用的视网膜血管数据集上进行了广泛的实验，结果表明VFGS-Net相较于最先进的方法实现了竞争性的或更优的表现。 特别地，我们的模型在细小血管、复杂分支模式和低对比度区域上的分割准确性方面表现出了持续的改进，这突显了其鲁棒性和临床应用潜力。
URL
https://arxiv.org/abs/2602.10978
