WGAN domain adaptation for the joint optic disc-and-cup segmentation in fundus images

The cup-to-disc ratio (CDR), a clinical metric of the relative size of the optic cup to the optic disc, is a key indicator of glaucoma, a chronic eye disease leading to loss of vision. CDR can be measured from fundus images through the segmentation of optic disc and optic cup . Deep convolutional networks have been proposed to achieve biomedical image segmentation with less time and more accuracy, but requires large amounts of annotated training data on a target domain, which is often unavailable. Unsupervised domain adaptation framework alleviates this problem through leveraging off-the-shelf labeled data from its relevant source domains, which is realized by learning domain invariant features and improving the generalization capabilities of the segmentation model.

In this paper, we propose a WGAN domain adaptation framework for detecting optic disc-and-cup boundary in fundus images. Specifically, we build a novel adversarial domain adaptation framework that is guided by Wasserstein distance, therefore with better stability and convergence than typical adversarial methods. We finally evaluate our approach on publicly available datasets.

Our experiments show that the proposed approach improves Intersection-over-Union score for optic disc-and-cup segmentation, Dice score and reduces the root-mean-square error of cup-to-disc ratio, when we compare it with direct transfer learning and other state-of-the-art adversarial domain adaptation methods.

With this work, we demonstrate that WGAN guided domain adaptation obtains a state-of-the-art performance for the joint optic disc-and-cup segmentation in fundus images.