Deep Learning for Ventricular Arrhythmia Prediction Using Fibrosis Segmentations on Cardiac MRI Data.
Florence E. Van LieshoutRoel C. KleinMaarten Z. KolkKylian Van GeijtenbeekRomy VosSamuel Ruiperez-CampilloRuibin FengBrototo DebPrasanth GanesanReinoud KnopsIvana IsgumSanjiv NarayanErik BekkersBob VosFleur VY TjongPublished in: CinC (2022)
Keyphrases
- mri data
- deep learning
- mr imaging
- left ventricular
- magnetic resonance imaging
- mr images
- fully automatic
- medical images
- heart rate variability
- atrial fibrillation
- mri scans
- magnetic resonance images
- synthetic data
- machine learning
- heart disease
- manual segmentation
- white matter
- left ventricle
- unsupervised learning
- left atrium
- ecg signals
- image segmentation
- brain tissue
- weakly supervised
- short axis
- magnetic resonance
- mental models
- supervised learning
- medical imaging
- brain mr images
- semi automatic
- decision support system
- multiscale
- image data
- decision making
- data sets
- natural images