International ophthalmology · 2026
Explainable dual-stage context-aware SwinUNet for accurate optic disc and optic cup segmentation in glaucoma assessment.
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Gopi Kannan P, Balasubramanian C, Jarin T
Аннотация
PURPOSE: Accurate segmentation of glaucoma-related anatomical structures from retinal fundus images is crucial for reliable clinical assessment and early disease diagnosis. However, variations in illumination, low contrast, and complex structural patterns make precise boundary delineation of the optic disc (OD) and optic cup (OC) challenging. This study aims to improve the accuracy of OD and OC segmentation for glaucoma assessment.
METHODS: An Enhanced SwinUNet model is proposed, integrating hierarchical transformer-based feature extraction with a Dual-Stage Context-Aware Feature Refinement (DCF-Refine) module embedded in skip connections. A preprocessing stage is applied using CLAHE-based contrast enhancement in LAB color space along with min-max normalization to improve image quality and stabilize training. The model employs Swin Transformer (ST) blocks to capture both local structural details and long-range dependencies. The DCF-Refine module enhances feature fusion through sequential Spatial Context Refinement (SCR) and Channel Context Refinement (CCR).
RESULTS: Experimental evaluation on the Drishti-GS and REFUGE datasets demonstrates that the proposed Enhanced SwinUNet achieves superior performance compared to existing segmentation methods, attaining accuracies of 99.3% and 99.1%, respectively.
CONCLUSION: The proposed model provides highly accurate and reliable segmentation of OD and OC structures, effectively addressing challenges in retinal image analysis. Its strong performance supports improved glaucoma-related structural assessment and has potential for clinical application.
METHODS: An Enhanced SwinUNet model is proposed, integrating hierarchical transformer-based feature extraction with a Dual-Stage Context-Aware Feature Refinement (DCF-Refine) module embedded in skip connections. A preprocessing stage is applied using CLAHE-based contrast enhancement in LAB color space along with min-max normalization to improve image quality and stabilize training. The model employs Swin Transformer (ST) blocks to capture both local structural details and long-range dependencies. The DCF-Refine module enhances feature fusion through sequential Spatial Context Refinement (SCR) and Channel Context Refinement (CCR).
RESULTS: Experimental evaluation on the Drishti-GS and REFUGE datasets demonstrates that the proposed Enhanced SwinUNet achieves superior performance compared to existing segmentation methods, attaining accuracies of 99.3% and 99.1%, respectively.
CONCLUSION: The proposed model provides highly accurate and reliable segmentation of OD and OC structures, effectively addressing challenges in retinal image analysis. Its strong performance supports improved glaucoma-related structural assessment and has potential for clinical application.