Diseases such as diabetic retinopathy and age-related macular degeneration pose a significant risk to vision, highlighting the importance of precise segmentation of retinal vessels for the tracking and diagnosis of progression. However, existing vessel segmentation methods that heavily rely on encoder-decoder structures struggle to capture contextual information about retinal vessel configurations, leading to challenges in reconciling semantic disparities between encoder and decoder features. To address this, we propose a novel feature enhancement segmentation network (FES-Net) that achieves accurate pixel-wise segmentation without requiring additional image enhancement steps. FES-Net directly processes the input image and utilizes four prompt convolutional blocks (PCBs) during downsampling, complemented by a shallow upsampling approach to generate a binary mask for each class. We evaluate the performance of FES-Net on four publicly available state-of-the-art datasets: DRIVE, STARE, CHASE, and HRF. The evaluation results clearly demonstrate the superior performance of FES-Net compared to other competitive approaches documented in the existing literature.

翻译：糖尿病视网膜病变和年龄相关性黄斑变性等疾病对视力构成显著风险，凸显了视网膜血管精确分割在病程追踪与诊断中的重要性。然而，现有严重依赖编码器-解码器结构的血管分割方法难以捕捉视网膜血管构型的上下文信息，导致编码器与解码器特征之间的语义差异难以调和。为解决这一问题，我们提出了一种新型特征增强分割网络（FES-Net），该网络能够在无需额外图像增强步骤的情况下实现精确的像素级分割。FES-Net直接处理输入图像，在下采样过程中采用四个提示卷积模块（PCBs），并辅以浅层上采样方法为每个类别生成二值掩码。我们在四个公开的最新基准数据集（DRIVE、STARE、CHASE和HRF）上评估了FES-Net的性能。评估结果明确显示，与现有文献中记载的其他竞争性方法相比，FES-Net具有更优越的性能。