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Categorization of collagen type I and II blend hydrogel using multipolarization SHG imaging with ResNet regression
Previously, the discrimination of collagen types I and II was successfully achieved using peptide pitch angle and anisotropic parameter methods. However, these methods require fitting polarization second harmonic generation (SHG) pixel-wise information into generic mathematical models, revealing inc...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636134/ https://www.ncbi.nlm.nih.gov/pubmed/37945626 http://dx.doi.org/10.1038/s41598-023-46417-0 |
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author | Nair, Anupama Lin, Chun-Yu Hsu, Feng-Chun Wong, Ta-Hsiang Chuang, Shu-Chun Lin, Yi-Shan Chen, Chung-Hwan Campagnola, Paul Lien, Chi-Hsiang Chen, Shean-Jen |
author_facet | Nair, Anupama Lin, Chun-Yu Hsu, Feng-Chun Wong, Ta-Hsiang Chuang, Shu-Chun Lin, Yi-Shan Chen, Chung-Hwan Campagnola, Paul Lien, Chi-Hsiang Chen, Shean-Jen |
author_sort | Nair, Anupama |
collection | PubMed |
description | Previously, the discrimination of collagen types I and II was successfully achieved using peptide pitch angle and anisotropic parameter methods. However, these methods require fitting polarization second harmonic generation (SHG) pixel-wise information into generic mathematical models, revealing inconsistencies in categorizing collagen type I and II blend hydrogels. In this study, a ResNet approach based on multipolarization SHG imaging is proposed for the categorization and regression of collagen type I and II blend hydrogels at 0%, 25%, 50%, 75%, and 100% type II, without the need for prior time-consuming model fitting. A ResNet model, pretrained on 18 progressive polarization SHG images at 10° intervals for each percentage, categorizes the five blended collagen hydrogels with a mean absolute error (MAE) of 0.021, while the model pretrained on nonpolarization images exhibited 0.083 MAE. Moreover, the pretrained models can also generally regress the blend hydrogels at 20%, 40%, 60%, and 80% type II. In conclusion, the multipolarization SHG image-based ResNet analysis demonstrates the potential for an automated approach using deep learning to extract valuable information from the collagen matrix. |
format | Online Article Text |
id | pubmed-10636134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106361342023-11-11 Categorization of collagen type I and II blend hydrogel using multipolarization SHG imaging with ResNet regression Nair, Anupama Lin, Chun-Yu Hsu, Feng-Chun Wong, Ta-Hsiang Chuang, Shu-Chun Lin, Yi-Shan Chen, Chung-Hwan Campagnola, Paul Lien, Chi-Hsiang Chen, Shean-Jen Sci Rep Article Previously, the discrimination of collagen types I and II was successfully achieved using peptide pitch angle and anisotropic parameter methods. However, these methods require fitting polarization second harmonic generation (SHG) pixel-wise information into generic mathematical models, revealing inconsistencies in categorizing collagen type I and II blend hydrogels. In this study, a ResNet approach based on multipolarization SHG imaging is proposed for the categorization and regression of collagen type I and II blend hydrogels at 0%, 25%, 50%, 75%, and 100% type II, without the need for prior time-consuming model fitting. A ResNet model, pretrained on 18 progressive polarization SHG images at 10° intervals for each percentage, categorizes the five blended collagen hydrogels with a mean absolute error (MAE) of 0.021, while the model pretrained on nonpolarization images exhibited 0.083 MAE. Moreover, the pretrained models can also generally regress the blend hydrogels at 20%, 40%, 60%, and 80% type II. In conclusion, the multipolarization SHG image-based ResNet analysis demonstrates the potential for an automated approach using deep learning to extract valuable information from the collagen matrix. Nature Publishing Group UK 2023-11-09 /pmc/articles/PMC10636134/ /pubmed/37945626 http://dx.doi.org/10.1038/s41598-023-46417-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Nair, Anupama Lin, Chun-Yu Hsu, Feng-Chun Wong, Ta-Hsiang Chuang, Shu-Chun Lin, Yi-Shan Chen, Chung-Hwan Campagnola, Paul Lien, Chi-Hsiang Chen, Shean-Jen Categorization of collagen type I and II blend hydrogel using multipolarization SHG imaging with ResNet regression |
title | Categorization of collagen type I and II blend hydrogel using multipolarization SHG imaging with ResNet regression |
title_full | Categorization of collagen type I and II blend hydrogel using multipolarization SHG imaging with ResNet regression |
title_fullStr | Categorization of collagen type I and II blend hydrogel using multipolarization SHG imaging with ResNet regression |
title_full_unstemmed | Categorization of collagen type I and II blend hydrogel using multipolarization SHG imaging with ResNet regression |
title_short | Categorization of collagen type I and II blend hydrogel using multipolarization SHG imaging with ResNet regression |
title_sort | categorization of collagen type i and ii blend hydrogel using multipolarization shg imaging with resnet regression |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636134/ https://www.ncbi.nlm.nih.gov/pubmed/37945626 http://dx.doi.org/10.1038/s41598-023-46417-0 |
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