Automatic Segment and Quantify Choroid Layer in Myopic eyes: Deep Learning based Model.

Hsiao, Chung-Hao; Huang, Yu-Len; Tse, Siu-Lun; Hsia, Wei-Ping; Chen, Hung-Ju; Cheng, Yuan-Shao; Chang, Chia-Jen

Hsiao, Chung-Hao; Huang, Yu-Len; Tse, Siu-Lun; Hsia, Wei-Ping; Chen, Hung-Ju; Cheng, Yuan-Shao; Chang, Chia-Jen.

Afiliação

Hsiao CH; Department of Ophthalmology, Taichung Veterans General Hospital, Taichung, Taiwan.
Huang YL; Department of Computer Science, Tunghai University, Taichung, Taiwan.
Tse SL; Department of Computer Science, Tunghai University, Taichung, Taiwan.
Hsia WP; Department of Ophthalmology, Taichung Veterans General Hospital, Taichung, Taiwan.
Chen HJ; Department of Ophthalmology, Taichung Veterans General Hospital, Taichung, Taiwan.
Cheng YS; Department of Ophthalmology, Taichung Veterans General Hospital, Taichung, Taiwan.
Chang CJ; Department of Ophthalmology, Taichung Veterans General Hospital, Taichung, Taiwan.

Semin Ophthalmol ; 37(5): 611-618, 2022 Jul 04.

Article em En | MEDLINE | ID: mdl-35138208

Assuntos

Aprendizado Profundo; Miopia; Erros de Refração; Corioide; Humanos; Miopia/diagnóstico; Estudos Retrospectivos; Tomografia de Coerência Óptica/métodos

Palavras-chave

Deep-learning; Mask R-CNN; choroidal thickness; optical coherence tomography; refractive error

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Erros de Refração / Aprendizado Profundo / Miopia Tipo de estudo: Diagnostic_studies / Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Semin Ophthalmol Assunto da revista: OFTALMOLOGIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Taiwan

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