Feature Enhancer Segmentation Network (FES-Net) for Vessel Segmentation

TL;DR

FES-Net is a novel vessel segmentation network that enhances feature extraction for retinal images, achieving superior accuracy without additional image processing, and effectively captures contextual information for precise segmentation.

Contribution

The paper introduces FES-Net, a new segmentation architecture with prompt convolutional blocks that improves contextual feature extraction for retinal vessel segmentation.

Findings

FES-Net outperforms existing methods on DRIVE, STARE, CHASE, and HRF datasets.

Achieves high accuracy without additional image enhancement steps.

Demonstrates superior segmentation performance compared to state-of-the-art approaches.

Abstract

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.