G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection

TL;DR

This paper introduces G1020, a large, publicly available retinal fundus image dataset with comprehensive annotations, aimed at improving automated glaucoma detection and serving as a standard benchmark for future research.

Contribution

The paper presents G1020, a new large-scale retinal fundus image dataset with detailed annotations, addressing limitations of existing datasets and facilitating real-world glaucoma diagnosis research.

Findings

G1020 dataset contains 1020 high-resolution images with extensive ground truth annotations.

Baseline experiments demonstrate the dataset's utility for automated glaucoma diagnosis.

The dataset supports segmentation and diagnosis tasks, advancing AI applications in ophthalmology.

Abstract

Scarcity of large publicly available retinal fundus image datasets for automated glaucoma detection has been the bottleneck for successful application of artificial intelligence towards practical Computer-Aided Diagnosis (CAD). A few small datasets that are available for research community usually suffer from impractical image capturing conditions and stringent inclusion criteria. These shortcomings in already limited choice of existing datasets make it challenging to mature a CAD system so that it can perform in real-world environment. In this paper we present a large publicly available retinal fundus image dataset for glaucoma classification called G1020. The dataset is curated by conforming to standard practices in routine ophthalmology and it is expected to serve as standard benchmark dataset for glaucoma detection. This database consists of 1020 high resolution colour fundus images and provides ground truth annotations for glaucoma diagnosis, optic disc and optic cup segmentation, vertical cup-to-disc ratio, size of neuroretinal rim in inferior, superior, nasal and temporal quadrants, and bounding box location for optic disc. We also report baseline results by conducting extensive experiments for automated glaucoma diagnosis and segmentation of optic disc and optic cup.