Joint learning of motion estimation and segmentation for cardiac MR image sequences
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a novel deep learning method for joint estimation of motion and segmentation from cardiac MR image sequences. The proposed network consists of two branches: a cardiac motion estimation branch which is built on a novel unsupervised Siamese style recurrent spatial transformer network, and a cardiac segmentation branch that is based on a fully convolutional network. In particular, a joint multi-scale feature encoder is learned by optimizing the segmentation branch and the motion estimation branch simultaneously. This enables the weakly-supervised segmentation by taking advantage of features that are unsupervisedly learned in the motion estimation branch from a large amount of unannotated data. Experimental results using cardiac MlRI images from 220 subjects show that the joint learning of both tasks is complementary and the proposed models outperform the competing methods significantly in terms of accuracy and speed.
Date Issued
2018-09-16
Date Acceptance
2018-05-24
Citation
2018, 11071 LNCS, pp.472-480
ISSN
0302-9743
Publisher
Springer Verlag
Start Page
472
End Page
480
Journal / Book Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
11071 LNCS
Copyright Statement
© Springer Nature Switzerland AG 2018. . The final publication is available at Springer via https://link.springer.com/chapter/10.1007%2F978-3-030-00934-2_53
Source
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
Subjects
cs.CV
cs.CV
Artificial Intelligence & Image Processing
08 Information and Computing Sciences
Publication Status
Published
Date Publish Online
2018-09-26