Macular hole morphology and measurement using an automated three-dimensional image segmentation algorithm

OBJECTIVE: Full-thickness macular holes (MH) are classified principally by size, which is one of the strongest predictors of anatomical and visual success. Using a three-dimensional (3D) automated image processing algorithm, we analysed optical coherence tomography (OCT) images of 104 MH of patients...

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Detalles Bibliográficos
Autores principales: Chen, Yunzi, Nasrulloh, Amar V, Wilson, Ian, Geenen, Caspar, Habib, Maged, Obara, Boguslaw, Steel, David H W
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7430427/
https://www.ncbi.nlm.nih.gov/pubmed/32844119
http://dx.doi.org/10.1136/bmjophth-2019-000404

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7430427/
https://www.ncbi.nlm.nih.gov/pubmed/32844119
http://dx.doi.org/10.1136/bmjophth-2019-000404

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