Deep learning for abdominal adipose tissue segmentation with few labelled samples.

Wang, Zheng; Hounye, Alphonse Houssou; Zhang, Jianglin; Hou, Muzhou; Qi, Min

Wang, Zheng; Hounye, Alphonse Houssou; Zhang, Jianglin; Hou, Muzhou; Qi, Min.

Afiliação

Wang Z; School of Mathematics and Statistics, Central South University, Changsha, 410083, China.
Hounye AH; Science and Engineering School, Hunan First Normal University, Changsha, 410205, China.
Zhang J; School of Mathematics and Statistics, Central South University, Changsha, 410083, China.
Hou M; Department of Detmatology, The Second Clinical Medical College, Shenzhen Peoples Hospital, Jinan University. The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China.
Qi M; School of Mathematics and Statistics, Central South University, Changsha, 410083, China. houmuzhou@sina.com.

Int J Comput Assist Radiol Surg ; 17(3): 579-587, 2022 Mar.

Article em En | MEDLINE | ID: mdl-34845590

Assuntos

Aprendizado Profundo; Gordura Abdominal/diagnóstico por imagem; Humanos; Redes Neurais de Computação; Gordura Subcutânea; Tomografia Computadorizada por Raios X

Palavras-chave

Computed tomography (CT); Convolutional neural network (ConvNet); Subcutaneous adipose tissue (SAT); Visceral adipose tissue (VAT)

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Int J Comput Assist Radiol Surg Assunto da revista: RADIOLOGIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

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