Using deep learning for the automated identification of cone and rod photoreceptors from adaptive optics imaging of the human retina.

Zhou, Mengxi; Doble, Nathan; Choi, Stacey S; Jin, Tianyu; Xu, Chenwei; Parthasarathy, Srinivasan; Ramnath, Rajiv

Zhou, Mengxi; Doble, Nathan; Choi, Stacey S; Jin, Tianyu; Xu, Chenwei; Parthasarathy, Srinivasan; Ramnath, Rajiv.

Afiliação

Zhou M; The Ohio State University, Department of Computer Science and Engineering, 2015 Neil Ave., Columbus, OH 43210, USA.
Doble N; The Ohio State University, College of Optometry, 338 W 10th Ave., Columbus, OH 43210, USA.
Choi SS; The Ohio State University, Department of Ophthalmology and Visual Science, Havener Eye Institute, 915 Olentangy River Road, Columbus, OH 43212, USA.
Jin T; The Ohio State University, College of Optometry, 338 W 10th Ave., Columbus, OH 43210, USA.
Xu C; The Ohio State University, Department of Ophthalmology and Visual Science, Havener Eye Institute, 915 Olentangy River Road, Columbus, OH 43212, USA.
Parthasarathy S; The Ohio State University, Department of Computer Science and Engineering, 2015 Neil Ave., Columbus, OH 43210, USA.
Ramnath R; The Ohio State University, Department of Statistics, 127 Pomerene Hall, 1760 Neil Ave, Columbus, OH 43212, USA.

Biomed Opt Express ; 13(10): 5082-5097, 2022 Oct 01.

Article em En | MEDLINE | ID: mdl-36425636

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Biomed Opt Express Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

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