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Conditional generative adversarial networks for the prediction of cardiac contraction from individual frames

Abstract:

Cardiac anatomy and function are interrelated in many ways,and these relations can be affected by multiple pathologies. In particular,this applies to ventricular shape and mechanical deformation. We pro-pose a machine learning approach to capture these interactions by using a conditional Generative Adversarial Network (cGAN) to predict cardiac deformation from individual Cardiac Magnetic Resonance (CMR) frames, learning a deterministic mapping between end-diastolic (ED) to end-systolic...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-39074-7_12

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
St Cross College
Role:
Author
Publisher:
Springer Publisher's website
Journal:
Statistical Atlases and Computational Modelling of the Heart 2019 Journal website
Host title:
STACOM: International Workshop on Statistical Atlases and Computational Models of the Heart 2019
Publication date:
2020-01-23
Acceptance date:
2019-09-20
Event title:
10th International Workshop of Statistical Atlases and Computational Modelling (STACOM 2019)
Event location:
Shenzhen, China
Event website:
https://stacom2019.cardiacatlas.org/
Event start date:
2019-10-13T00:00:00Z
DOI:
Source identifiers:
1069089
Language:
English
Keywords:
Pubs id:
pubs:1069089
UUID:
uuid:4b743fcd-50d7-4705-8708-352b341e4a49
Local pid:
pubs:1069089
Deposit date:
2019-11-01

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