A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI

Medical Image Analysis

Volumn 30, Issue , 2016, Pages 108-119

  Avendi, M R ^a,b   Kheradvar, Arash ^b   Jafarkhani, Hamid ^a  

^a University of California at Irvine   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Caridac MRI; Deep learning; Deformable models; LV segmentation; Machine learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; DEFORMATION; HEART; LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING;

AUTOMATIC SEGMENTATIONS; CARDIAC MAGNETIC RESONANCE IMAGING; CONVOLUTIONAL NETWORKS; DEEP LEARNING; DEFORMABLE MODELING; DEFORMABLE MODELS; EJECTION FRACTION; STATE-OF-THE-ART METHODS;

LEARNING ALGORITHMS;

ACCURACY; ARTICLE; CARDIOVASCULAR MAGNETIC RESONANCE; HEART LEFT VENTRICLE; HUMAN; LEARNING ALGORITHM; PRIORITY JOURNAL; ALGORITHM; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; CINE MAGNETIC RESONANCE IMAGING; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; DIAGNOSTIC IMAGING; EVALUATION STUDY; HEART LEFT VENTRICLE FUNCTION; HEART VENTRICLE; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; MACHINE LEARNING; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; VALIDATION STUDY;

ALGORITHMS; COMPUTER SIMULATION; HEART VENTRICLES; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING, CINE; MODELS, CARDIOVASCULAR; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE; VENTRICULAR DYSFUNCTION, LEFT;

EID: 84958955334     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2016.01.005     Document Type: Article

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