Convolutional autoencoder joint boundary and mask adversarial learning for fundus image segmentation

The precise segmentation of the optic cup (OC) and the optic disc (OD) is important for glaucoma screening. In recent years, medical image segmentation based on convolutional neural networks (CNN) has achieved remarkable results. However, many traditional CNN methods do not consider the cross-domain problem, i.e., generalization on datasets of different domains. In this paper, we propose a novel unsupervised domain-adaptive segmentation architecture called CAE-BMAL. Firstly, we enhance the source domain with a convolutional autoencoder to improve the generalization ability of the model. Then, we introduce an adversarial learning-based boundary discrimination branch to reduce the impact of the complex environment during segmentation. Finally, we evaluate the proposed method on three datasets, Drishti-GS, RIM-ONE-r3, and REFUGE. The experimental evaluations outperform most state-of-the-art methods in accuracy and generalization. We further evaluate the cup-to-disk ratio performance in OD and OC segmentation, which indicates the effectiveness of glaucoma discrimination.