GlauNet: Glaucoma Diagnosis for OCTA Imaging Using a New CNN Architecture

Abstract

Glaucoma is a neurodegenerative disease that affects the optic nerve head and causes visual field defect. Current investigations focus on neural component which may overlook other important factors such as the vascular cause. The optical coherence tomography angiography (OCTA) imaging has been developed and provided quantitative parameters that showed good diagnostic accuracy to detect glaucoma. However, those parameters are based on image processing of observed clinical findings, therefore, some image information can be lost. Convolutional neural network has been successfully applied for automatic feature extraction and object classification. In this study, the glaucoma diagnosis network, namely GlauNet, has been proposed. GlauNet consists of two sections: the feature-extraction section and the classification section. The feature-extraction section has three convolutional layers. Each convolutional layer is followed by rectified linear unit and maximum pooling layer. The classification section contains five fully connected layers. GlauNet was trained with 258 glaucomatous and 439 non-glaucomatous eyes. The visualization of the feature-extraction section showed the highlight in the area of optic nerve head and retinal nerve fiber layer in the superotemporal and inferotemporal regions. It was then tested on 27 glaucomatous and 48 non-glaucomatous eyes. Its sensitivity and specificity were 88.9% with 89.6%, respectively. The area under receiver operating characteristic curve of GlauNet was 0.89. GlauNet was robust against the artifacts. Its sensitivity and specificity were still higher than 80% (82.4% and 80.3%, respectively) when tested on 88 poor-quality images.

Keywords

• Artificial intelligence
• convolutional neural network
• deep learning
• glaucoma
• optical coherence tomography angiography
• retinal blood vessels