Combined Optic Disc and Optic Cup Segmentation Network Based on Adversarial Learning

Abstract

Glaucoma is a group of diseases characterized by progressive optic nerve damage, ultimately resulting in irreversible visual impairment. Early diagnosis through color fundus photography, including measurement of the vertical cup-to-disk ratio (CDR), can help prevent vision loss. The normal range of CDR values is usually 0.3-0.5, and if it exceeds 0.6, then there may be some problems. However, asymmetrical thinning at the edges of the bottom-superior temporal-nasal region and large gaps in datasets pose challenges for existing automatic segmentation methods. To address these challenges, this paper proposes a joint segmentation method for the optic disc (OD) and optic cup (OC) based on an adversarial network, incorporating new monitoring functions to guide the network optimization process. The effectiveness and stability of this framework were evaluated using two public performance datasets of retinal fundus images, namely Drishti-GS and REFUGE. On the Drishti-GS dataset, our method achieved a score of 0.850/0.964/0.086, while on the REFUGE dataset, it obtained a score of 0.887/0.975/0.061. These results indicate the effectiveness of our approach.

Keywords

• Adversarial learning
• deep learning
• color fundus photography
• glaucoma