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

• URL: http://arxiv.org/abs/2309.03535v1
• Date: Thu, 7 Sep 2023 07:46:46 GMT
• Title: Feature Enhancer Segmentation Network (FES-Net) for Vessel Segmentation
• Authors: Tariq M. Khan, Muhammad Arsalan, Shahzaib Iqbal, Imran Razzak, Erik Meijering
• Abstract summary: 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. We evaluate the performance of FES-Net on four publicly available state-of-the-art datasets: DRIVE, STARE, CHASE, and HRF.
• Score: 19.455350961592742
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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