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AxoNet: A deep learning-based tool to count retinal ganglion cell axons
In this work, we develop a robust, extensible tool to automatically and accurately count retinal ganglion cell axons in optic nerve (ON) tissue images from various animal models of glaucoma. We adapted deep learning to regress pixelwise axon count density estimates, which were then integrated over t...
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/PMC7228952/ https://www.ncbi.nlm.nih.gov/pubmed/32415269 http://dx.doi.org/10.1038/s41598-020-64898-1 |
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author | Ritch, Matthew D. Hannon, Bailey G. Read, A. Thomas Feola, Andrew J. Cull, Grant A. Reynaud, Juan Morrison, John C. Burgoyne, Claude F. Pardue, Machelle T. Ethier, C. Ross |
author_facet | Ritch, Matthew D. Hannon, Bailey G. Read, A. Thomas Feola, Andrew J. Cull, Grant A. Reynaud, Juan Morrison, John C. Burgoyne, Claude F. Pardue, Machelle T. Ethier, C. Ross |
author_sort | Ritch, Matthew D. |
collection | PubMed |
description | In this work, we develop a robust, extensible tool to automatically and accurately count retinal ganglion cell axons in optic nerve (ON) tissue images from various animal models of glaucoma. We adapted deep learning to regress pixelwise axon count density estimates, which were then integrated over the image area to determine axon counts. The tool, termed AxoNet, was trained and evaluated using a dataset containing images of ON regions randomly selected from whole cross sections of both control and damaged rat ONs and manually annotated for axon count and location. This rat-trained network was then applied to a separate dataset of non-human primate (NHP) ON images. AxoNet was compared to two existing automated axon counting tools, AxonMaster and AxonJ, using both datasets. AxoNet outperformed the existing tools on both the rat and NHP ON datasets as judged by mean absolute error, R(2) values when regressing automated vs. manual counts, and Bland-Altman analysis. AxoNet does not rely on hand-crafted image features for axon recognition and is robust to variations in the extent of ON tissue damage, image quality, and species of mammal. Therefore, AxoNet is not species-specific and can be extended to quantify additional ON characteristics in glaucoma and potentially other neurodegenerative diseases. |
format | Online Article Text |
id | pubmed-7228952 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-72289522020-05-26 AxoNet: A deep learning-based tool to count retinal ganglion cell axons Ritch, Matthew D. Hannon, Bailey G. Read, A. Thomas Feola, Andrew J. Cull, Grant A. Reynaud, Juan Morrison, John C. Burgoyne, Claude F. Pardue, Machelle T. Ethier, C. Ross Sci Rep Article In this work, we develop a robust, extensible tool to automatically and accurately count retinal ganglion cell axons in optic nerve (ON) tissue images from various animal models of glaucoma. We adapted deep learning to regress pixelwise axon count density estimates, which were then integrated over the image area to determine axon counts. The tool, termed AxoNet, was trained and evaluated using a dataset containing images of ON regions randomly selected from whole cross sections of both control and damaged rat ONs and manually annotated for axon count and location. This rat-trained network was then applied to a separate dataset of non-human primate (NHP) ON images. AxoNet was compared to two existing automated axon counting tools, AxonMaster and AxonJ, using both datasets. AxoNet outperformed the existing tools on both the rat and NHP ON datasets as judged by mean absolute error, R(2) values when regressing automated vs. manual counts, and Bland-Altman analysis. AxoNet does not rely on hand-crafted image features for axon recognition and is robust to variations in the extent of ON tissue damage, image quality, and species of mammal. Therefore, AxoNet is not species-specific and can be extended to quantify additional ON characteristics in glaucoma and potentially other neurodegenerative diseases. Nature Publishing Group UK 2020-05-15 /pmc/articles/PMC7228952/ /pubmed/32415269 http://dx.doi.org/10.1038/s41598-020-64898-1 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Ritch, Matthew D. Hannon, Bailey G. Read, A. Thomas Feola, Andrew J. Cull, Grant A. Reynaud, Juan Morrison, John C. Burgoyne, Claude F. Pardue, Machelle T. Ethier, C. Ross AxoNet: A deep learning-based tool to count retinal ganglion cell axons |
title | AxoNet: A deep learning-based tool to count retinal ganglion cell axons |
title_full | AxoNet: A deep learning-based tool to count retinal ganglion cell axons |
title_fullStr | AxoNet: A deep learning-based tool to count retinal ganglion cell axons |
title_full_unstemmed | AxoNet: A deep learning-based tool to count retinal ganglion cell axons |
title_short | AxoNet: A deep learning-based tool to count retinal ganglion cell axons |
title_sort | axonet: a deep learning-based tool to count retinal ganglion cell axons |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7228952/ https://www.ncbi.nlm.nih.gov/pubmed/32415269 http://dx.doi.org/10.1038/s41598-020-64898-1 |
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