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Multiscale graph convolutional networks for cardiac motion analysis

Abstract:

We propose a multiscale spatio-temporal graph convolutional network (MST-GCN) approach to learn the left ventricular (LV) motion patterns from cardiac MR image sequences. The MST-GCN follows an encoder-decoder framework. The encoder uses a sequence of multiscale graph computation units (MGCUs). The myocardial geometry is represented as a graph. The network models the internal relations of the graph nodes via feature extraction at different scales and fuses the feature across scales to form a ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-78710-3_26

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Role:
Author
ORCID:
0000-0002-0199-3783
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Role:
Author
ORCID:
0000-0003-2943-7698
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Role:
Author
ORCID:
0000-0002-5683-5889
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
St Hilda's College
Role:
Author
ORCID:
0000-0002-3060-3772
Publisher:
Springer Publisher's website
Pages:
264-272
Series:
Lecture Notes in Computer Science
Series number:
12738
Host title:
Functional Imaging and Modeling of the Heart: FIMH 2021
Publication date:
2021-06-18
Event title:
11th International Conference on Functional Imaging and Modeling of the Heart (FIMH 2021)
Event location:
Virtual event
Event website:
https://fimh2021.github.io/
Event start date:
2021-06-21T00:00:00Z
Event end date:
2021-06-25T00:00:00Z
DOI:
EISBN:
978-3-030-78710-3
ISBN:
978-3-030-78709-7
Language:
English
Keywords:
Pubs id:
1191215
Local pid:
pubs:1191215
Deposit date:
2022-05-31

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