Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance

Medical Image Analysis

Volumn 35, Issue , 2017, Pages 159-171

  Ngo, Tuan Anh ^a   Lu, Zhi ^b   Carneiro, Gustavo ^c  

^a VIETNAM NATIONAL UNIVERSITY OF AGRICULTURE   (Viet Nam)

^b UNIVERSITY OF SOUTH AUSTRALIA   (Australia)

^c UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

Cardiac cine magnetic resonance; Deep learning; Level set method; Segmentation of the left ventricle of the heart

Indexed keywords

AUTOMATION; DROP BREAKUP; EVOLUTIONARY ALGORITHMS; HEART; HYDROGELS; IMAGE SEGMENTATION; LEVEL MEASUREMENT; MAGNETIC LEVITATION VEHICLES; MAGNETIC RESONANCE; MAGNETISM; MEDICAL IMAGING; MEDICAL PROBLEMS; NUMERICAL METHODS;

AUTOMATED SEGMENTATION; FULLY AUTOMATED; LEARNING METHODS; LEFT VENTRICLES; LEVEL SET METHOD; SEGMENTATION RESULTS; SEMI-AUTOMATED; STATE OF THE ART;

DEEP LEARNING;

ALGORITHM; ARTICLE; AUTOMATION; CARDIAC CINE MAGNETIC RESONANCE; CARDIOVASCULAR MAGNETIC RESONANCE; CONTROLLED STUDY; DATA BASE; ENDOCARDIUM; EPICARDIUM; HEART CYCLE; HEART FAILURE; HEART LEFT VENTRICLE; HEART LEFT VENTRICLE HYPERTROPHY; HEART MUSCLE ISCHEMIA; HUMAN; INTERMETHOD COMPARISON; LEARNING; MEASUREMENT ACCURACY; MODEL; PRIORITY JOURNAL; AUTOMATED PATTERN RECOGNITION; CINE MAGNETIC RESONANCE IMAGING; DIAGNOSTIC IMAGING; HEART VENTRICLE; IMAGE PROCESSING; MACHINE LEARNING; PROCEDURES; REPRODUCIBILITY;

HEART VENTRICLES; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING, CINE; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS;

EID: 84978204565     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2016.05.009     Document Type: Article

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