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

Qin C, Bai W, Schlemper J, Petersen SE, Piechnik SK, Neubauer S, Rueckert D (2018)

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Publication Type: Conference contribution

Publication year: 2018

Journal

Publisher: Springer Verlag

Book Volume: 11071 LNCS

Pages Range: 472-480

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Granada, ESP

ISBN: 9783030009335

DOI: 10.1007/978-3-030-00934-2_53

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.

Involved external institutions

How to cite

APA:

Qin, C., Bai, W., Schlemper, J., Petersen, S.E., Piechnik, S.K., Neubauer, S., & Rueckert, D. (2018). Joint learning of motion estimation and segmentation for cardiac MR image sequences. In Gabor Fichtinger, Christos Davatzikos, Carlos Alberola-López, Alejandro F. Frangi, Julia A. Schnabel (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 472-480). Granada, ESP: Springer Verlag.

MLA:

Qin, Chen, et al. "Joint learning of motion estimation and segmentation for cardiac MR image sequences." Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Gabor Fichtinger, Christos Davatzikos, Carlos Alberola-López, Alejandro F. Frangi, Julia A. Schnabel, Springer Verlag, 2018. 472-480.

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