Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach

Magnetic Resonance in Medicine

Volumn 78, Issue 6, 2017, Pages 2439-2448

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

^a UNIVERSITY OF CALIFORNIA   (United States)

^b University of California at Irvine   (United States)

Author keywords

cardiac MRI; deep learning; deformable models; right ventricle; segmentation

Indexed keywords

CONVOLUTIONAL NEURAL NETWORKS; DEEP LEARNING; DEFORMATION; HEART; IMAGE SEGMENTATION; LEARNING SYSTEMS; MAGNETIC RESONANCE;

AUTOMATIC SEGMENTATIONS; CARDIAC MRI; DEFORMABLE MODELS; INITIAL SEGMENTATION; INTERNATIONAL SOCIETY; LEARNING-BASED APPROACH; QUANTITATIVE METRICS; RIGHT VENTRICLE;

LEARNING ALGORITHMS;

CARDIOVASCULAR MAGNETIC RESONANCE; CLINICAL ARTICLE; DATA BASE; HEART EJECTION FRACTION; HEART LEFT VENTRICLE ENDDIASTOLIC VOLUME; HEART RIGHT VENTRICLE; HUMAN; LEARNING ALGORITHM; MODEL; NERVOUS SYSTEM; QUANTITATIVE STUDY; REST; ALGORITHM; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; AUTOMATION; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; HEART; HEART VENTRICLE; IMAGE PROCESSING; MACHINE LEARNING; NUCLEAR MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY; STATISTICAL MODEL;

ALGORITHMS; AUTOMATION; HEART; HEART VENTRICLES; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGE PROCESSING, COMPUTER-ASSISTED; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS;

EID: 85013271835     PISSN: 07403194     EISSN: 15222594     Source Type: Journal    
DOI: 10.1002/mrm.26631     Document Type: Article

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