Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

IEEE Transactions on Biomedical Engineering

Volumn 63, Issue 7, 2016, Pages 1505-1516

  Wu, Guorong ^a   Kim, Minjeong ^a   Wang, Qian ^b   Munsell, Brent C ^c   Shen, Dinggang ^a,d  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

^c Department of Teacher Education and Department of Sociology and Anthropology   (United States)

^d Korea University   (South Korea)

Author keywords

deep learning; Deformable image registration; Hierarchical feature representation

Indexed keywords

DEFORMATION; FEATURE EXTRACTION; IMAGE REGISTRATION; LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING; UNSUPERVISED LEARNING;

CORRESPONDENCE DETECTION; DEEP LEARNING; DEFORMABLE IMAGE REGISTRATION; DISCRIMINATIVE FEATURES; FEATURE SELECTION METHODS; HIERARCHICAL FEATURES; REGISTRATION PERFORMANCE; UNSUPERVISED LEARNING METHOD;

MEDICAL IMAGING;

BRAIN; HUMAN; IMAGE ANALYSIS; IMAGING; LEARNING; REGISTRATION; ALGORITHM; DIAGNOSTIC IMAGING; IMAGE PROCESSING; NUCLEAR MAGNETIC RESONANCE IMAGING; PROCEDURES; UNSUPERVISED MACHINE LEARNING;

ALGORITHMS; BRAIN; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; UNSUPERVISED MACHINE LEARNING;

EID: 84978033606     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2015.2496253     Document Type: Article

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