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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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

Fan Guo¹, Wentao Liu¹, Jin Tang¹

¹School of Automation, Central South University, Changsha, 410083, China; Xiangjiang Laboratory, Changsha 410205, China.

Medical Image Analysis

|October 11, 2025

Summary

This summary is machine-generated.

This study introduces a novel saliency detection method for segmenting optic cup and disc in glaucoma screening. The approach enhances accuracy in medical image analysis for early disease detection.

Keywords:

Computer-aided diagnosis Edge-assisted feature extraction Glaucoma diagnosis Global context information enhancement Multi-scale feature fusion OC and OD Segmentation Saliency detection

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Area of Science:

Ophthalmology
Medical Image Analysis
Computer Vision

Background:

Glaucoma is a leading cause of blindness globally, necessitating early diagnosis.
Accurate optic cup and disc segmentation is crucial for glaucoma screening via cup-to-disc ratio (CDR) calculation.
Traditional methods struggle with complex fundus images due to vessel interference.

Purpose of the Study:

To develop an advanced method for optic cup and disc segmentation in fundus images.
To improve the accuracy and robustness of glaucoma screening tools.
To extend saliency detection to a three-class segmentation task.

Main Methods:

A saliency detection-inspired approach for three-class segmentation (optic cup, optic disc, background).
Integration of an Edge-guided Multi-scale Feature Extraction Module (EMFEM), Global Context Information Enhancement Module (GCIEM), and Self-Interaction Module (SIM).
Utilized a ConvNeXtV2 backbone and advanced loss functions (Cross-Entropy, CEL, EAL) for optimized performance.

Main Results:

The proposed method outperformed mainstream segmentation algorithms on six public datasets.
Achieved highest Dice coefficients: 0.9073 (optic cup, Drishti-GS) and 0.9734 (optic cup, Rim-One).
Demonstrated strong robustness and generalizability in optic disc segmentation (0.8965 on Rim-One).

Conclusions:

The developed method offers a promising advancement for glaucoma-assisted diagnosis.
The approach provides a robust solution for medical image segmentation tasks.
Highlights the potential of saliency detection in improving ophthalmic image analysis.