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Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease

Stargardt disease is one of the most common forms of inherited retinal disease and leads to permanent vision loss. A diagnostic feature of the disease is retinal flecks, which appear hyperautofluorescent in fundus autofluorescence (FAF) imaging. The size and number of these flecks increase with dise...

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Autores principales: Charng, Jason, Xiao, Di, Mehdizadeh, Maryam, Attia, Mary S., Arunachalam, Sukanya, Lamey, Tina M., Thompson, Jennifer A., McLaren, Terri L., De Roach, John N., Mackey, David A., Frost, Shaun, Chen, Fred K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536408/
https://www.ncbi.nlm.nih.gov/pubmed/33020556
http://dx.doi.org/10.1038/s41598-020-73339-y
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author Charng, Jason
Xiao, Di
Mehdizadeh, Maryam
Attia, Mary S.
Arunachalam, Sukanya
Lamey, Tina M.
Thompson, Jennifer A.
McLaren, Terri L.
De Roach, John N.
Mackey, David A.
Frost, Shaun
Chen, Fred K.
author_facet Charng, Jason
Xiao, Di
Mehdizadeh, Maryam
Attia, Mary S.
Arunachalam, Sukanya
Lamey, Tina M.
Thompson, Jennifer A.
McLaren, Terri L.
De Roach, John N.
Mackey, David A.
Frost, Shaun
Chen, Fred K.
author_sort Charng, Jason
collection PubMed
description Stargardt disease is one of the most common forms of inherited retinal disease and leads to permanent vision loss. A diagnostic feature of the disease is retinal flecks, which appear hyperautofluorescent in fundus autofluorescence (FAF) imaging. The size and number of these flecks increase with disease progression. Manual segmentation of flecks allows monitoring of disease, but is time-consuming. Herein, we have developed and validated a deep learning approach for segmenting these Stargardt flecks (1750 training and 100 validation FAF patches from 37 eyes with Stargardt disease). Testing was done in 10 separate Stargardt FAF images and we observed a good overall agreement between manual and deep learning in both fleck count and fleck area. Longitudinal data were available in both eyes from 6 patients (average total follow-up time 4.2 years), with both manual and deep learning segmentation performed on all (n = 82) images. Both methods detected a similar upward trend in fleck number and area over time. In conclusion, we demonstrated the feasibility of utilizing deep learning to segment and quantify FAF lesions, laying the foundation for future studies using fleck parameters as a trial endpoint.
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spelling pubmed-75364082020-10-07 Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease Charng, Jason Xiao, Di Mehdizadeh, Maryam Attia, Mary S. Arunachalam, Sukanya Lamey, Tina M. Thompson, Jennifer A. McLaren, Terri L. De Roach, John N. Mackey, David A. Frost, Shaun Chen, Fred K. Sci Rep Article Stargardt disease is one of the most common forms of inherited retinal disease and leads to permanent vision loss. A diagnostic feature of the disease is retinal flecks, which appear hyperautofluorescent in fundus autofluorescence (FAF) imaging. The size and number of these flecks increase with disease progression. Manual segmentation of flecks allows monitoring of disease, but is time-consuming. Herein, we have developed and validated a deep learning approach for segmenting these Stargardt flecks (1750 training and 100 validation FAF patches from 37 eyes with Stargardt disease). Testing was done in 10 separate Stargardt FAF images and we observed a good overall agreement between manual and deep learning in both fleck count and fleck area. Longitudinal data were available in both eyes from 6 patients (average total follow-up time 4.2 years), with both manual and deep learning segmentation performed on all (n = 82) images. Both methods detected a similar upward trend in fleck number and area over time. In conclusion, we demonstrated the feasibility of utilizing deep learning to segment and quantify FAF lesions, laying the foundation for future studies using fleck parameters as a trial endpoint. Nature Publishing Group UK 2020-10-05 /pmc/articles/PMC7536408/ /pubmed/33020556 http://dx.doi.org/10.1038/s41598-020-73339-y Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Charng, Jason
Xiao, Di
Mehdizadeh, Maryam
Attia, Mary S.
Arunachalam, Sukanya
Lamey, Tina M.
Thompson, Jennifer A.
McLaren, Terri L.
De Roach, John N.
Mackey, David A.
Frost, Shaun
Chen, Fred K.
Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease
title Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease
title_full Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease
title_fullStr Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease
title_full_unstemmed Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease
title_short Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease
title_sort deep learning segmentation of hyperautofluorescent fleck lesions in stargardt disease
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536408/
https://www.ncbi.nlm.nih.gov/pubmed/33020556
http://dx.doi.org/10.1038/s41598-020-73339-y
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