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Using Retinal Imaging to Study Dementia

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Graph convolutional network based optic disc and cup segmentation on fundus images.

Zhiqiang Tian¹, Yaoyue Zheng¹, Xiaojian Li¹

¹School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Biomedical Optics Express

|July 9, 2020

Summary

This summary is machine-generated.

This study introduces a novel graph convolutional network (GCN) for segmenting optic disc (OD) and optic cup (OC) in glaucoma screening. The method achieves superior performance on benchmark datasets, enhancing diagnostic accuracy.

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Glaucoma screening relies on clinical features, including the optic cup-to-disc ratio.
Accurate segmentation of the optic disc (OD) and optic cup (OC) is crucial for this measurement.
Existing methods may require improvement in segmentation accuracy and efficiency.

Purpose of the Study:

To develop and evaluate a novel graph convolutional network (GCN) based method for OD and OC segmentation.
To improve the accuracy of optic disc and optic cup segmentation for glaucoma screening.
To compare the proposed method against state-of-the-art techniques on public datasets.

Main Methods:

A multi-scale convolutional neural network (CNN) was employed as a feature map extractor.
A graph convolutional network (GCN) utilized the extracted feature maps for segmentation.
The GCN input consisted of concatenated feature maps and graph nodes.

Main Results:

On the REFUGE dataset, the method achieved a Jaccard index of 95.64% for OD and 91.60% for OC.
Dice similarity coefficients (DSC) reached 97.76% for OD and 95.58% for OC on the REFUGE dataset.
The proposed GCN-based method demonstrated superior performance compared to state-of-the-art methods on both REFUGE and Drishthi-GS1 datasets.

Conclusions:

The proposed GCN-based approach effectively segments the optic disc and optic cup.
This method shows significant potential for enhancing glaucoma screening through improved image analysis.
The findings suggest that GCNs are a powerful tool for medical image segmentation tasks in ophthalmology.