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ThicknessTool: automated ImageJ retinal layer thickness and profile in digital images
To develop an automated retina layer thickness measurement tool for the ImageJ platform, to quantitate nuclear layers following the retina contour. We developed the ThicknessTool (TT), an automated thickness measurement plugin for the ImageJ platform. To calibrate TT, we created a calibration datase...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7595229/ https://www.ncbi.nlm.nih.gov/pubmed/33116161 http://dx.doi.org/10.1038/s41598-020-75501-y |
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author | Maidana, Daniel E. Notomi, Shoji Ueta, Takashi Zhou, Tianna Joseph, Danica Kosmidou, Cassandra Caminal-Mitjana, Josep Maria Miller, Joan W. Vavvas, Demetrios G. |
author_facet | Maidana, Daniel E. Notomi, Shoji Ueta, Takashi Zhou, Tianna Joseph, Danica Kosmidou, Cassandra Caminal-Mitjana, Josep Maria Miller, Joan W. Vavvas, Demetrios G. |
author_sort | Maidana, Daniel E. |
collection | PubMed |
description | To develop an automated retina layer thickness measurement tool for the ImageJ platform, to quantitate nuclear layers following the retina contour. We developed the ThicknessTool (TT), an automated thickness measurement plugin for the ImageJ platform. To calibrate TT, we created a calibration dataset of mock binary skeletonized mask images with increasing thickness masks and different rotations. Following, we created a training dataset and performed an agreement analysis of thickness measurements between TT and two masked manual observers. Finally, we tested the performance of TT measurements in a validation dataset of retinal detachment images. In the calibration dataset, there were no differences in layer thickness between measured and known thickness masks, with an overall coefficient of variation of 0.00%. Training dataset measurements of immunofluorescence retina nuclear layers disclosed no significant differences between TT and any observer’s average outer nuclear layer (ONL) (p = 0.998), inner nuclear layer (INL) (p = 0.807), and ONL/INL ratio (p = 0.944) measurements. Agreement analysis showed that bias between TT vs. observers’ mean was lower than between any observers’ mean against each other in the ONL (0.77 ± 0.34 µm vs 3.25 ± 0.33 µm) and INL (1.59 ± 0.28 µm vs 2.82 ± 0.36 µm). Validation dataset showed that TT can detect significant and true ONL thinning (p = 0.006), more sensitive than manual measurement capabilities (p = 0.069). ThicknessTool can measure retina nuclear layers thickness in a fast, accurate, and precise manner with multi-platform capabilities. In addition, the TT can be customized to user preferences and is freely available to download. |
format | Online Article Text |
id | pubmed-7595229 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75952292020-10-29 ThicknessTool: automated ImageJ retinal layer thickness and profile in digital images Maidana, Daniel E. Notomi, Shoji Ueta, Takashi Zhou, Tianna Joseph, Danica Kosmidou, Cassandra Caminal-Mitjana, Josep Maria Miller, Joan W. Vavvas, Demetrios G. Sci Rep Article To develop an automated retina layer thickness measurement tool for the ImageJ platform, to quantitate nuclear layers following the retina contour. We developed the ThicknessTool (TT), an automated thickness measurement plugin for the ImageJ platform. To calibrate TT, we created a calibration dataset of mock binary skeletonized mask images with increasing thickness masks and different rotations. Following, we created a training dataset and performed an agreement analysis of thickness measurements between TT and two masked manual observers. Finally, we tested the performance of TT measurements in a validation dataset of retinal detachment images. In the calibration dataset, there were no differences in layer thickness between measured and known thickness masks, with an overall coefficient of variation of 0.00%. Training dataset measurements of immunofluorescence retina nuclear layers disclosed no significant differences between TT and any observer’s average outer nuclear layer (ONL) (p = 0.998), inner nuclear layer (INL) (p = 0.807), and ONL/INL ratio (p = 0.944) measurements. Agreement analysis showed that bias between TT vs. observers’ mean was lower than between any observers’ mean against each other in the ONL (0.77 ± 0.34 µm vs 3.25 ± 0.33 µm) and INL (1.59 ± 0.28 µm vs 2.82 ± 0.36 µm). Validation dataset showed that TT can detect significant and true ONL thinning (p = 0.006), more sensitive than manual measurement capabilities (p = 0.069). ThicknessTool can measure retina nuclear layers thickness in a fast, accurate, and precise manner with multi-platform capabilities. In addition, the TT can be customized to user preferences and is freely available to download. Nature Publishing Group UK 2020-10-28 /pmc/articles/PMC7595229/ /pubmed/33116161 http://dx.doi.org/10.1038/s41598-020-75501-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 Maidana, Daniel E. Notomi, Shoji Ueta, Takashi Zhou, Tianna Joseph, Danica Kosmidou, Cassandra Caminal-Mitjana, Josep Maria Miller, Joan W. Vavvas, Demetrios G. ThicknessTool: automated ImageJ retinal layer thickness and profile in digital images |
title | ThicknessTool: automated ImageJ retinal layer thickness and profile in digital images |
title_full | ThicknessTool: automated ImageJ retinal layer thickness and profile in digital images |
title_fullStr | ThicknessTool: automated ImageJ retinal layer thickness and profile in digital images |
title_full_unstemmed | ThicknessTool: automated ImageJ retinal layer thickness and profile in digital images |
title_short | ThicknessTool: automated ImageJ retinal layer thickness and profile in digital images |
title_sort | thicknesstool: automated imagej retinal layer thickness and profile in digital images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7595229/ https://www.ncbi.nlm.nih.gov/pubmed/33116161 http://dx.doi.org/10.1038/s41598-020-75501-y |
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