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Joint learning of motion estimation and segmentation for cardiac MR image sequences
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![]() | Accepted version | 689.81 kB | Adobe PDF | View/Open |
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 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) |