Unsupervised Cross-modality Cardiac Image Segmentation via Disentangled Representation Learning and Consistency Regularization

Abstract

Deep neural networks based approaches for medical image segmentation rely heavily on the availability of large amount of annotated data, which sometimes is difficult to obtain due to time, logistic effort and the requirement of expertise knowledge. Unpaired image translation enables a cross-modality segmentation network to be trained in an annotation-poor target domain by leveraging an annotation-rich source domain but most existing methods separate the image translation stage from the image segmentation stage and are not trained end-to-end. In this paper, we propose an end-to-end unsupervised cross-modality cardiac image segmentation method, taking advantage of diverse image translation via disentangled representation learning and consistency regularization in one network. Different from learning one-to-one mapping, our method characterizes the complex relationship between domains as many-to-many mapping. A novel diverse inter-domain semantic consistency loss is then proposed to regularize the cross-modality segmentation process. We additionally introduce an intra-domain semantic consistency loss to encourage the segmentation consistency between the original input and the image after cross-cycle reconstruction. We conduct comprehensive experiments on two publicly available datasets to evaluate the effectiveness of the proposed method. The experimental results demonstrate the efficacy of the present approach.