Validation of automated artificial intelligence segmentation of optical coherence tomography images

PURPOSE: To benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera. METHODS: A convolutional neural network (CNN) was trai...

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Detalhes bibliográficos
Publicado no:PLoS One
Main Authors: Maloca, Peter M., Lee, Aaron Y., de Carvalho, Emanuel R., Okada, Mali, Fasler, Katrin, Leung, Irene, Hörmann, Beat, Kaiser, Pascal, Suter, Susanne, Hasler, Pascal W., Zarranz-Ventura, Javier, Egan, Catherine, Heeren, Tjebo F. C., Balaskas, Konstantinos, Tufail, Adnan, Scholl, Hendrik P. N.
Formato: Artigo
Idioma:Inglês
Publicado em: Public Library of Science 2019
Assuntos:
Research Article
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC6697318/
https://ncbi.nlm.nih.gov/pubmed/31419240
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0220063
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