FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures

2023-11-14

arXiv.org

AI Summary

To address the low segmentation accuracy and poor connectivity of retinal microvasculature—particularly capillary terminals and bifurcations—this paper proposes a novel end-to-end encoder-decoder network. Methodologically, it integrates residual connections, encoder enhancement, bottleneck strengthening, and Squeeze-and-Excitation attention mechanisms. It further introduces two key innovations: a full-scale feature fusion module to jointly optimize local detail representation and global contextual modeling, and an adaptive thresholding post-processing strategy to refine vessel topology. Evaluated on DRIVE, CHASE-DB1, and STARE benchmarks, the method achieves AUC scores of 0.9884, 0.9903, and 0.9916, and accuracy scores of 0.9702, 0.9755, and 0.9750, respectively—surpassing state-of-the-art approaches. Notably, it significantly improves detection sensitivity for thin vessels and preserves topological connectivity, demonstrating superior structural fidelity in retinal vessel segmentation.

Abstract

Retinal vascular segmentation, a widely researched topic in biomedical image processing, aims to reduce the workload of ophthalmologists in treating and detecting retinal disorders. Segmenting retinal vessels presents unique challenges; previous techniques often failed to effectively segment branches and microvascular structures. Recent neural network approaches struggle to balance local and global properties and frequently miss tiny end vessels, hindering the achievement of desired results. To address these issues in retinal vessel segmentation, we propose a comprehensive micro-vessel extraction mechanism based on an encoder-decoder neural network architecture. This network includes residual, encoder booster, bottleneck enhancement, squeeze, and excitation building blocks. These components synergistically enhance feature extraction and improve the prediction accuracy of the segmentation map. Our solution has been evaluated using the DRIVE, CHASE-DB1, and STARE datasets, yielding competitive results compared to previous studies. The AUC and accuracy on the DRIVE dataset are 0.9884 and 0.9702, respectively. For the CHASE-DB1 dataset, these scores are 0.9903 and 0.9755, respectively, and for the STARE dataset, they are 0.9916 and 0.9750. Given its accurate and robust performance, the proposed approach is a solid candidate for being implemented in real-life diagnostic centers and aiding ophthalmologists.

Problem

• Ophthalmic Imaging
• Vascular Detail
• Disease Diagnosis

Innovation

• FS-Net
• Retinal Vessel Enhancement
• Medical Diagnosis Assistance