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Abstract #4787

Simultaneous learning of group-wise registration and joint segmentation of cardiac cine MRI with sparse annotation

Junwei Yang1,2, Pengfang Qian2, Thomas Kuestner3, Pietro Lio1, Dinggang Shen2,4, and Haikun Qi2
1Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom, 2School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, 3Department of Interventional and Diagnostic Radiology, University Hospital of Tuebingen, Tuebingen, Germany, 4Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China


Numerous deep learning methods have been proposed for cardiac cine MRI segmentation, while most of them require laborious annotation for supervised training. Herein, we propose an approach to perform group-wise registration and joint segmentation of cardiac cine images, by training a registration network in a self-supervised manner to align dynamic images to their mean image space and also a segmentation network in a weakly-supervised manner using sparsely-annotated data and predicted motions from the registration network. By training these two (registration and segmentation) networks simultaneously, our proposed joint learning approach provides better segmentations than the direct segmentation network.

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