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Joint learning of motion estimation and segmentation for cardiac MR image sequences

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Title: Joint learning of motion estimation and segmentation for cardiac MR image sequences
Authors: Qin, C
Bai, W
Schlemper, J
Petersen, SE
Piechnik, SK
Neubauer, S
Rueckert, D
Item Type: Journal Article
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.
Issue Date: 16-Sep-2018
Date of Acceptance: 24-May-2018
URI: http://hdl.handle.net/10044/1/71241
DOI: https://dx.doi.org/10.1007/978-3-030-00934-2_53
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
Keywords: cs.CV
Artificial Intelligence & Image Processing
08 Information and Computing Sciences
Publication Status: Published
Online Publication Date: 2018-09-26
Appears in Collections:Computing
Department of Medicine (up to 2019)