Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification.
Alex BrattJiwon KimMeridith PollieAshley N BeecyNathan H TehraniNoel CodellaRocio Perez-JohnstonMaria Chiara PalumboJavid AlakbarliWayne ColizzaIan R DrexlerClerio F AzevedoRaymond J KimRichard B DevereuxJonathan W WeinsaftPublished in: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance (2019)
Fully automated machine learning PC-CMR segmentation performs robustly for aortic flow quantification - yielding rapid segmentation, small differences with manual segmentation, and identification of differential forward/left ventricular volumetric stroke volume in context of concomitant mitral regurgitation. Findings support use of machine learning for analysis of large scale CMR datasets.
Keyphrases
- machine learning
- deep learning
- convolutional neural network
- left ventricular
- artificial intelligence
- magnetic resonance
- big data
- aortic valve
- atrial fibrillation
- heart failure
- pulmonary artery
- acute myocardial infarction
- high throughput
- magnetic resonance imaging
- brain injury
- transcatheter aortic valve replacement
- pulmonary hypertension
- subarachnoid hemorrhage