Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets

Medical Physics

Volumn 43, Issue 4, 2016, Pages 1882-1886

  Cha, Kenny H ^a   Hadjiiski, Lubomir ^a   Samala, Ravi K ^a   Chan, Heang Ping ^a   Caoili, Elaine M ^a   Cohan, Richard H ^a  

^a UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

Author keywords

Bladder; Computer aided detection; CT urography; Deep learning; Level set; Segmentation

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER AIDED ANALYSIS; COMPUTER AIDED DIAGNOSIS; COMPUTER AIDED INSTRUCTION; CONVOLUTION; DECISION TREES; DEEP LEARNING; IMAGE SEGMENTATION; NEURAL NETWORKS; NUMERICAL METHODS;

BLADDER; COMPUTER AIDED DETECTION; CONVOLUTIONAL NEURAL NETWORK; CT UROGRAPHY; DEEP-LEARNING; HAAR FEATURES; LEVEL SET; LIKELIHOOD MAP; SEGMENTATION; UROGRAPHY;

COMPUTERIZED TOMOGRAPHY;

BLADDER; CLASSIFIER; FEASIBILITY STUDY; HUMAN; LEARNING; MAJOR CLINICAL STUDY; NERVOUS SYSTEM; RANDOM FOREST; UROGRAPHY; ARTIFICIAL NEURAL NETWORK; DIAGNOSTIC IMAGING; IMAGE PROCESSING; PROCEDURES; STANDARD; STATISTICAL MODEL; X-RAY COMPUTED TOMOGRAPHY;

HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LIKELIHOOD FUNCTIONS; NEURAL NETWORKS (COMPUTER); REFERENCE STANDARDS; TOMOGRAPHY, X-RAY COMPUTED; URINARY BLADDER; UROGRAPHY;

EID: 84973293116     PISSN: 00942405     EISSN: 24734209     Source Type: Journal    
DOI: 10.1118/1.4944498     Document Type: Article

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