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Dynamic spatio-temporal graph convolutional networks for cardiac motion analysis

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...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ISBI48211.2021.9433890

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Institution:
University of Oxford
Department:
ENGINEERING SCIENCE
Sub department:
Engineering Science
Oxford college:
St Hildas College; St Hildas College; St Hildas College; St Hildas College; St Hildas College; St Hildas College; St Hildas College; St Hildas College; St Hildas College
Role:
Author
ORCID:
0000-0002-3060-3772
Publisher:
IEEE
Host title:
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Pages:
122-125
Publication date:
2021-05-25
Acceptance date:
2021-01-08
Event title:
IEEE ISBI 2021 International Symposium on Biomedical Imaging
Event location:
Virtual Event
Event website:
https://biomedicalimaging.org/2021/
Event start date:
2021-04-13
Event end date:
2021-04-16
DOI:
EISSN:
1945-8452
ISSN:
1945-7928
EISBN:
978-1-6654-1246-9
ISBN:
978-1-6654-2947-4
Language:
English
Keywords:
Pubs id:
1182625
Local pid:
pubs:1182625
Deposit date:
2021-08-09

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