Encoding atlases by randomized classification forests for efficient multi-atlas label propagation

Medical Image Analysis

Volumn 18, Issue 8, 2014, Pages 1262-1273

  Zikic, D ^a   Glocker, B ^b   Criminisi, A ^a  

^a MICROSOFT RESEARCH   (United Kingdom)

^b IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Brain; Multi atlas label propagation; Randomized forest; Segmentation

Indexed keywords

BRAIN; ENCODING (SYMBOLS); IMAGE FUSION; IMAGE SEGMENTATION;

COMPUTATIONAL COSTS; DEEP CLASSIFICATIONS; LABEL PROPAGATION; LARGE DATABASE; PROBABILISTIC ATLAS; RANDOMIZED FOREST; STATE OF THE ART; TARGET IMAGES;

FORESTRY;

ALGORITHM; ANATOMY AND HISTOLOGY; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; DOCUMENTATION; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; NUCLEAR MAGNETIC RESONANCE IMAGING; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; DOCUMENTATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 84926277285     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2014.06.010     Document Type: Article

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