  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.
