Boundary Restored Network for Subpleural Pulmonary Lesion Segmentation on Ultrasound Images at Local and Global Scales.

Xu, Yupeng; Zhang, Yi; Bi, Ke; Ning, Zhiyu; Xu, Lisha; Shen, Mengjun; Deng, Guoying; Wang, Yin

Xu, Yupeng; Zhang, Yi; Bi, Ke; Ning, Zhiyu; Xu, Lisha; Shen, Mengjun; Deng, Guoying; Wang, Yin.

Afiliação

Xu Y; Department of Ophthalmology, Shanghai General Hospital, National Clinical Research Center for Eye Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Ocular Fundus Disease, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, 200080, China.
Zhang Y; Department of Ultrasound, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China.
Bi K; Department of Pathology, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200092, China.
Ning Z; School of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia.
Xu L; Department of Ultrasound, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China.
Shen M; Department of Ultrasound, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China.
Deng G; Trauma Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China. guoying.deng@qq.com.
Wang Y; Department of Ultrasound, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China. lpbbl@aliyun.com.

J Digit Imaging ; 33(5): 1155-1166, 2020 10.

Article em En | MEDLINE | ID: mdl-32556913

Assuntos

Processamento de Imagem Assistida por Computador; Detecção Precoce de Câncer; Humanos; Pulmão/diagnóstico por imagem; Neoplasias Pulmonares/diagnóstico por imagem; Redes Neurais de Computação

Palavras-chave

Convolutional neural network (CNN); Deep learning; Image segmentation; Subpleural pulmonary lesion (SPL) segmentation; Ultrasound image

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador Tipo de estudo: Diagnostic_studies / Screening_studies Limite: Humans Idioma: En Revista: J Digit Imaging Assunto da revista: DIAGNOSTICO POR IMAGEM / INFORMATICA MEDICA / RADIOLOGIA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: China

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