Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

IEEE Transactions on Medical Imaging

Volumn 35, Issue 4, 2016, Pages 1077-1089

  Guo, Yanrong ^a   Gao, Yaozong ^a   Shen, Dinggang ^a,b  

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

^b Korea University   (South Korea)

Author keywords

Deformable model; MR prostate segmentation; sparse patch matching; stacked sparse auto encoder (SSAE)

Indexed keywords

DEFORMATION; DISEASES; IMAGE MATCHING; IMAGE SEGMENTATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING;

AUTO ENCODERS; DEEP FEATURE LEARNING; DEFORMABLE MODELING; DEFORMABLE SEGMENTATION; FEATURE REPRESENTATION; PATCH MATCHING; PROSTATE LOCALIZATION; PROSTATE SEGMENTATION;

UROLOGY;

EXPERIMENTAL MODEL; LEARNING; LEARNING ALGORITHM; MODEL; PROSTATE; ALGORITHM; DIAGNOSTIC IMAGING; HUMAN; MACHINE LEARNING; MALE; NUCLEAR MAGNETIC RESONANCE IMAGING; PROCEDURES;

ALGORITHMS; HUMANS; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING; MALE; PROSTATE;

EID: 84963878431     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2015.2508280     Document Type: Article

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