Conference item
Conditional generative adversarial networks for the prediction of cardiac contraction from individual frames
- Abstract:
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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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- Files:
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(Accepted manuscript, pdf, 1.2MB)
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- Publisher copy:
- 10.1007/978-3-030-39074-7_12
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Authors
Bibliographic Details
- 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:
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1069089
Item Description
- Language:
- English
- Keywords:
- Pubs id:
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pubs:1069089
- UUID:
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uuid:4b743fcd-50d7-4705-8708-352b341e4a49
- Local pid:
- pubs:1069089
- Deposit date:
- 2019-11-01
Terms of use
- Copyright holder:
- Springer Nature
- Copyright date:
- 2020
- Rights statement:
- © Springer Nature Switzerland AG 2020.
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from Springer at: https://doi.org/10.1007/978-3-030-39074-7_12
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