Accuracy, uncertainty, and adaptability of automatic myocardial ASL segmentation using deep CNN.

Do, Hung P; Guo, Yi; Yoon, Andrew J; Nayak, Krishna S

Do, Hung P; Guo, Yi; Yoon, Andrew J; Nayak, Krishna S.

Afiliação

Do HP; Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California.
Guo Y; Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California.
Yoon AJ; Long Beach Memorial Medical Center, University of California Irvine, Irvine, California.
Nayak KS; Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California.

Magn Reson Med ; 83(5): 1863-1874, 2020 05.

Article em En | MEDLINE | ID: mdl-31729078

Assuntos

Processamento de Imagem Assistida por Computador; Redes Neurais de Computação; Humanos; Imageamento por Ressonância Magnética; Miocárdio; Incerteza

Palavras-chave

Bayesian; Monte Carlo dropout; automatic segmentation; deep convolutional neural network; false-positive and false-negative tradeoff; uncertainty measure

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Redes Neurais de Computação Tipo de estudo: Guideline / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

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