A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis.

Shi, Danli; Lin, Zhihong; Wang, Wei; Tan, Zachary; Shang, Xianwen; Zhang, Xueli; Meng, Wei; Ge, Zongyuan; He, Mingguang

Shi, Danli; Lin, Zhihong; Wang, Wei; Tan, Zachary; Shang, Xianwen; Zhang, Xueli; Meng, Wei; Ge, Zongyuan; He, Mingguang.

Afiliación

Shi D; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Lin Z; Faculty of Engineering, Monash University, Melbourne, VIC, Australia.
Wang W; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Tan Z; Centre for Eye Research Australia, East Melbourne, VIC, Australia.
Shang X; Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Eye Institute, Guangdong Academy of Medical Sciences, Guangzhou, China.
Zhang X; Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Eye Institute, Guangdong Academy of Medical Sciences, Guangzhou, China.
Meng W; Guangzhou Vision Tech Medical Technology Co., Ltd., Guangzhou, China.
Ge Z; Research Center and Faculty of Engineering, Monash University, Melbourne, VIC, Australia.
He M; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.

Front Cardiovasc Med ; 9: 823436, 2022.

Article en En | MEDLINE | ID: mdl-35391847

Palabras clave

artificial intelligence; automated analysis; cardiovascular disease; epidemiology; hierarchical vessel morphology

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Cardiovasc Med Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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