IOSUDA: an unsupervised domain adaptation with input and output space alignment for joint optic disc and cup segmentation

摘要

The segmentation of the optic disc (OD) and the optic cup (OC) is an important step for glaucoma diagnosis. Conventional deep neural network models appear good performance, but degradation when facing domain shift. In this paper, we propose a novel unsupervised domain adaptation framework, called Input and Output Space Unsupervised Domain Adaptation (IOSUDA), to reduce the performance degradation in joint OD and OC segmentation. Our framework achieves both the input and output space alignments. Precisely, we extract the shared content features and the style features of each domain through image translation. The shared content features are input to the segmentation network, then we conduct adversarial learning to promote the similarity of segmentation maps from different domains. Results of the comparative experiments on three different fundus image datasets show that our IOSUDA outperforms the other tested methods in unsupervised domain adaptation. The code of the proposed model is available at https://github.com/EdisonCCL/IOSUDA.