Deep convolutional neural networks for multi-modality isointense infant brain image segmentation

NeuroImage

Volumn 108, Issue , 2015, Pages 214-224

  Zhang, Wenlu ^a   Li, Rongjian ^a   Deng, Houtao ^b   Wang, Li ^c   Lin, Weili ^c   Ji, Shuiwang ^a   Shen, Dinggang ^c,d  

^a ECS Building   (United States)

^b Instacart ^*  (United States)

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

^d Korea University   (South Korea)

Author keywords

Convolutional neural networks; Deep learning; Image segmentation; Infant brain image; Multi modality data

Indexed keywords

ANALYTIC METHOD; ARTICLE; CEREBROSPINAL FLUID; COMPARATIVE EFFECTIVENESS; CONVOLUTIONAL NEURAL NETWORK; COUPLED LEVEL SET; DIFFUSION WEIGHTED IMAGING; FRACTIONAL ANISOTROPY; HUMAN; IMAGE ANALYSIS; INFANT; INTERMETHOD COMPARISON; MAJORITY VOTING METHOD; MEASUREMENT ACCURACY; NERVE CELL NETWORK; NEUROIMAGING; NORMAL HUMAN; PERFORMANCE MEASUREMENT SYSTEM; RANDOM FOREST; SUPPORT VECTOR MACHINE; ANATOMY AND HISTOLOGY; ANISOTROPY; ARTIFICIAL NEURAL NETWORK; BRAIN; BRAIN MAPPING; GRAY MATTER; IMAGE PROCESSING; NUCLEAR MAGNETIC RESONANCE IMAGING; PROCEDURES; WHITE MATTER;

ANISOTROPY; BRAIN; BRAIN MAPPING; GRAY MATTER; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; INFANT; MAGNETIC RESONANCE IMAGING; NEURAL NETWORKS (COMPUTER); WHITE MATTER;

EID: 84921492033     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2014.12.061     Document Type: Article

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