Dynamic spatio-temporal graph convolutional networks for cardiac motion analysis

Lu P, Bai W, Rueckert D, Noble JA (2021)

Publication Type: Conference contribution

Publication year: 2021

Journal

Proceedings - International Symposium on Biomedical Imaging

Publisher: IEEE Computer Society

Book Volume: 2021-April

Pages Range: 122-125

Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging

Event location: Nice, FRA

ISBN: 9781665412469

DOI: 10.1109/ISBI48211.2021.9433890

Abstract

We propose a dynamic spatio-temporal graph convolutional network (DST-GCN) approach to learn the left ventricular (LV) motion patterns from cardiac MR cine images. We represent the myocardial geometry using a graph that is constructed from sample nodes on endo-and epicardial contours. The DST-GCN follows an encoder-decoder framework. The encoder accepts a given cardiac motion represented by a sequence of ST-GCN. The decoder employs a graph-based gated recurrent unit (G-GRU) to predict future cardiac motion. We show that the DST-GCN can automatically quantify the spatio-temporal patterns in cardiac MR that characterise cardiac motion. Experiments are performed on the UK Biobank dataset. We compare four methods from two architecture variances. Experiments show that the proposed method inputting node velocities with residual connection in the decoder outperform others, and achieves a mean squared error of 0.135 pixel between the ground truth node locations and our prediction.

Involved external institutions

University of Oxford GB United Kingdom (GB) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Lu, P., Bai, W., Rueckert, D., & Noble, J.A. (2021). Dynamic spatio-temporal graph convolutional networks for cardiac motion analysis. In Proceedings - International Symposium on Biomedical Imaging (pp. 122-125). Nice, FRA: IEEE Computer Society.

MLA:

Lu, Ping, et al. "Dynamic spatio-temporal graph convolutional networks for cardiac motion analysis." Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021, Nice, FRA IEEE Computer Society, 2021. 122-125.

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