In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@inproceedings{Qin:2018:10.1007/978-3-030-00934-2_53,
author = {Qin, C and Bai, W and Schlemper, J and Petersen, SE and Piechnik, SK and Neubauer, S and Rueckert, D},
doi = {10.1007/978-3-030-00934-2_53},
pages = {472--480},
publisher = {Springer Verlag},
title = {Joint learning of motion estimation and segmentation for cardiac MR image sequences},
url = {http://dx.doi.org/10.1007/978-3-030-00934-2_53},
year = {2018}
}
TY - CPAPER
AB - 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.
AU - Qin,C
AU - Bai,W
AU - Schlemper,J
AU - Petersen,SE
AU - Piechnik,SK
AU - Neubauer,S
AU - Rueckert,D
DO - 10.1007/978-3-030-00934-2_53
EP - 480
PB - Springer Verlag
PY - 2018///
SP - 472
TI - Joint learning of motion estimation and segmentation for cardiac MR image sequences
UR - http://dx.doi.org/10.1007/978-3-030-00934-2_53
UR - http://hdl.handle.net/10044/1/71241
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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