ArticleiScience2024
W-Net: A boundary-aware cascade network for robust and accurate optic disc segmentation.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Cross-gated feature interaction network for fetal abdominal circumference image segmentation.Scientific reports · 2026Article
- Automated Optic Disc Tilt Classification in Fundus Photographs Using Segmentation and the Elliptical Ratio: External Clinical Validation Study.JMIR formative research · 2026Article
- FunduSegmenter: Leveraging the RETFound Foundation Model for Joint Optic Disc and Optic Cup Segmentation in Retinal Fundus Images.Translational vision science & technology · 2026Article
- ACLA-Net: attention-guided cross-level alignment network for MRI-based prostate cancer lesion segmentation.Frontiers in physiology · 2026Article
- An effective vessel segmentation method using SLOA-HGC.Scientific reports · 2025Article
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Authors and funding
6 authors.
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Abstract
Accurate optic disc (OD) segmentation has a great significance for computer-aided diagnosis of different types of eye diseases. Due to differences in image acquisition equipment and acquisition methods, the resolution, size, contrast, and clarity of images from different datasets show significant differences, resulting in poor generalization performance of deep learning networks. To solve this problem, this study proposes a multi-level segmentation network. The network includes data quality enhancement module (DQEM), coarse segmentation module (CSM), localization module (OLM), and fine segmentation stage module (FSM). In FSM, W-Net is proposed for the first time, and boundary loss is introduced in the loss function, which effectively improves the performance of OD segmentation. We generalized the model in the REFUGE test dataset, GAMMA dataset, Drishti-GS1 dataset, and IDRiD dataset, respectively. The results show that our method has the best OD segmentation performance in different datasets compared with state-of-the-art networks.
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