Deep Learning Applied to Automated Segmentation of Geographic Atrophy in Fundus Autofluorescence Images

PURPOSE: This study describes the development of a deep learning algorithm based on the U-Net architecture for automated segmentation of geographic atrophy (GA) lesions in fundus autofluorescence (FAF) images. METHODS: Image preprocessing and normalization by modified adaptive histogram equalization...

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Detalles Bibliográficos
Autores principales: Arslan, Janan, Samarasinghe, Gihan, Sowmya, Arcot, Benke, Kurt K., Hodgson, Lauren A. B., Guymer, Robyn H., Baird, Paul N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8267211/
https://www.ncbi.nlm.nih.gov/pubmed/34228106
http://dx.doi.org/10.1167/tvst.10.8.2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8267211/
https://www.ncbi.nlm.nih.gov/pubmed/34228106
http://dx.doi.org/10.1167/tvst.10.8.2

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