Selective Boundary-aware Skip Connections for High-precision Ophthalmic Image Segmentation
Year: 2025
Type: article
Abstract: Background: Ophthalmic image segmentation plays a key role in diagnosing retinal conditions. U-Net and its variants, though effective, often propagate redundant features that impair boundary accuracy. Objective: To propose a novel segmentation framework using a Boundary-Aware Gating Mechanism (BAGM) to enhance precision in retinal boundary delineation. Methods: The architecture integrates depth-wise separable convolutions, boundary-guided spatial attention, and a hybrid loss function (Dice + boundary-aware focal loss). The model was validated on OCT-500, DRIVE, and REFUGE datasets. Results: Our model achieved superior Dice score (0.923 ± 0.012), improved boundary F-score, and reduced Hausdorff distance, outperforming Attention U-Net and TransUNet while maintaining computational efficiency. Conclusion: The proposed dynamic boundary-gated architecture is both accurate and lightweight, suitable for real-time clinical settings such as OCT and fundus imaging.
Authors: Salma Sultan Alshahrani, Rana Abdulmohsen Alotaibi, Yazeed Sultan Alshahrani, Atheer Ali Alshadidi, Dalia Faisal Albalawi, Raneem Fadhel Asiri, Amani Abdulmanam Alali, Mansour Abdulrahman Mansour, Warif Nasser Alghofaily, Mohammad Al Mohaini
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4413280491