A saliency detection-inspired method for optic disc and cup segmentation.

Abstract

Glaucoma, as one of the leading causes of blindness worldwide, requires early diagnosis for effective patient treatment. Accurate segmentation of the optic cup and optic disc, along with the calculation of the cup-to-disc ratio (CDR), is central to glaucoma screening. However, traditional semantic segmentation methods face significant challenges in handling complex fundus images due to interference from background structures such as blood vessels. To address this, this paper proposes a saliency detection-inspired method for optic cup and disc segmentation, extending saliency detection to a three-class task (optic cup, optic disc, and background). The approach incorporates an Edge-guided Multi-scale Feature Extraction Module (EMFEM), a Global Context Information Enhancement Module (GCIEM), and a Self-Interaction Module (SIM) to integrate multi-level features and improve segmentation performance. Additionally, a ConvNeXtV2-based feature extraction network and improved loss functions-including Cross-Entropy Loss, Consistency-Enhanced Loss (CEL), and Edge-Gradient-Aware Tversky Loss (EAL)-are employed to optimize saliency focus and boundary refinement. Experimental results demonstrate that the proposed method outperforms mainstream segmentation algorithms on six public datasets. It achieves the highest Dice coefficients of 0.9073 for optic cup segmentation on the Drishti-GS dataset, and 0.9734 and 0.8965 for optic cup and disc segmentation on the Rim-One dataset, respectively. The method exhibits strong robustness and generalizability, offering a promising direction for glaucoma-assisted diagnosis and medical image segmentation.