Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification

Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification

Abstract Background Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing,...

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Bibliographic Details
Main Authors: Alex Bratt, Jiwon Kim, Meridith Pollie, Ashley N. Beecy, Nathan H. Tehrani, Noel Codella, Rocio Perez-Johnston, Maria Chiara Palumbo, Javid Alakbarli, Wayne Colizza, Ian R. Drexler, Clerio F. Azevedo, Raymond J. Kim, Richard B. Devereux, Jonathan W. Weinsaft
Format: Article
Language:English
Published: Elsevier 2019-01-01
Series:Journal of Cardiovascular Magnetic Resonance
Subjects:
Cardiovascular magnetic resonance
Machine learning
Deep learning
Phase contrast
Aorta
Online Access:http://link.springer.com/article/10.1186/s12968-018-0509-0

Internet

http://link.springer.com/article/10.1186/s12968-018-0509-0

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