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Cardiac motion estimation and segmentation play important roles inquantitatively assessing cardiac function and diagnosing cardiovasculardiseases. In this paper, we propose a novel deep learning method for jointestimation of motion and segmentation from cardiac MR image sequences. Theproposed network consists of two branches: a cardiac motion estimation branchwhich is built on a novel unsupervised Siamese style recurrent spatialtransformer network, and a cardiac segmentation branch that is based on a fullyconvolutional network. In particular, a joint multi-scale feature encoder islearned by optimizing the segmentation branch and the motion estimation branchsimultaneously. This enables the weakly-supervised segmentation by takingadvantage of features that are unsupervisedly learned in the motion estimationbranch from a large amount of unannotated data. Experimental results usingcardiac MRI images from 220 subjects show that the joint learning of both tasksis complementary and the proposed models outperform the competing methodssignificantly in terms of accuracy and speed.

Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences