Multi-atlas learner fusion: An efficient segmentation approach for large-scale data

Medical Image Analysis

Volumn 26, Issue 1, 2015, Pages 82-91

  Asman, Andrew J ^a   Huo, Yuankai ^a   Plassard, Andrew J ^a   Landman, Bennett A ^a,b  

^a VANDERBILT UNIVERSITY   (United States)

^b VANDERBILT UNIVERSITY   (United States)

Author keywords

AdaBoost; Machine learning; Multi atlas learner fusion; Multi atlas segmentation

Indexed keywords

ABILITY TESTING; ADAPTIVE BOOSTING; ARTIFICIAL INTELLIGENCE; BRAIN MAPPING; LEARNING SYSTEMS; STATISTICAL TESTS;

INITIAL SEGMENTATION; LOW-DIMENSIONAL REPRESENTATION; LOW-DIMENSIONAL SPACES; REPRODUCIBILITIES; SEGMENTATION ALGORITHMS; SEGMENTATION RESULTS; SMALL-SCALE MODELING; TARGET REGISTRATION;

IMAGE SEGMENTATION;

ACCURACY; ALGORITHM; ARTICLE; BRAIN; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; MULTI ATLAS LEARNER FUSION; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; REPRODUCIBILITY; SENSITIVITY ANALYSIS; STATISTICAL SIGNIFICANCE; ANATOMY AND HISTOLOGY; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; CONNECTOME; FACTUAL DATABASE; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; INFORMATION RETRIEVAL; MULTIMODAL IMAGING; PHYSIOLOGY; PROCEDURES; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; BRAIN; CONNECTOME; DATABASES, FACTUAL; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; MAGNETIC RESONANCE IMAGING; MULTIMODAL IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 84941279289     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2015.08.010     Document Type: Article

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