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Purpose
Glaucoma is a leading cause of irreversible blindness, and accurate cup-to-disc ratio (CDR) measurement is essential for early detection. This study presents an enhanced deep learning–based system for automated CDR estimation and glaucoma screening.
Methods
We propose an end-to-end framework consisting of three modules: (1) optic cup and disc segmentation using an enhanced dual encoder–decoder network (E-DCoAtUNet), (2) a conditional random field (CRF) post-processing module for boundary refinement, and (3) a measurement module for vertical CDR calculation and glaucoma classification. The model was trained and evaluated on the Drishti-GS dataset and validated on the REFUGE dataset to assess generalizability.
Results
The system achieved Dice scores of 97.6% for the optic disc and 90.8% for the optic cup, further improved by CRF refinement. Automated CDR estimation showed strong agreement with expert annotations (Pearson’s r = 0.9190, MAE = 0.0387). For glaucoma screening, the system demonstrated reliable performance across both datasets, highlighting its robustness and clinical applicability.
Conclusion
The proposed E-DCoAtUNet-based system provides a fully automated, interpretable, and precise solution for glaucoma screening. By integrating advanced segmentation, boundary refinement, and accurate measurement, it ensures consistent CDR evaluation even under challenging imaging conditions, and demonstrates strong potential for real-world clinical application.