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Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle
Currently available software tools for automated segmentation and analysis of muscle cross-section images often perform poorly in cases of weak or non-uniform staining conditions. To address these issues, our group has developed the MyoSAT (Myofiber Segmentation and Analysis Tool) image-processing p...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7757813/ https://www.ncbi.nlm.nih.gov/pubmed/33362264 http://dx.doi.org/10.1371/journal.pone.0243163 |
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author | Stevens, Courtney R. Berenson, Josh Sledziona, Michael Moore, Timothy P. Dong, Lynn Cheetham, Jonathan |
author_facet | Stevens, Courtney R. Berenson, Josh Sledziona, Michael Moore, Timothy P. Dong, Lynn Cheetham, Jonathan |
author_sort | Stevens, Courtney R. |
collection | PubMed |
description | Currently available software tools for automated segmentation and analysis of muscle cross-section images often perform poorly in cases of weak or non-uniform staining conditions. To address these issues, our group has developed the MyoSAT (Myofiber Segmentation and Analysis Tool) image-processing pipeline. MyoSAT combines several unconventional approaches including advanced background leveling, Perona-Malik anisotropic diffusion filtering, and Steger’s line detection algorithm to aid in pre-processing and enhancement of the muscle image. Final segmentation is based upon marker-based watershed segmentation. Validation tests using collagen V labeled murine and canine muscle tissue demonstrate that MyoSAT can determine mean muscle fiber diameter with an average accuracy of ~92.4%. The software has been tested to work on full muscle cross-sections and works well even under non-optimal staining conditions. The MyoSAT software tool has been implemented as a macro for the freely available ImageJ software platform. This new segmentation tool allows scientists to efficiently analyze large muscle cross-sections for use in research studies and diagnostics. |
format | Online Article Text |
id | pubmed-7757813 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-77578132021-01-06 Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle Stevens, Courtney R. Berenson, Josh Sledziona, Michael Moore, Timothy P. Dong, Lynn Cheetham, Jonathan PLoS One Research Article Currently available software tools for automated segmentation and analysis of muscle cross-section images often perform poorly in cases of weak or non-uniform staining conditions. To address these issues, our group has developed the MyoSAT (Myofiber Segmentation and Analysis Tool) image-processing pipeline. MyoSAT combines several unconventional approaches including advanced background leveling, Perona-Malik anisotropic diffusion filtering, and Steger’s line detection algorithm to aid in pre-processing and enhancement of the muscle image. Final segmentation is based upon marker-based watershed segmentation. Validation tests using collagen V labeled murine and canine muscle tissue demonstrate that MyoSAT can determine mean muscle fiber diameter with an average accuracy of ~92.4%. The software has been tested to work on full muscle cross-sections and works well even under non-optimal staining conditions. The MyoSAT software tool has been implemented as a macro for the freely available ImageJ software platform. This new segmentation tool allows scientists to efficiently analyze large muscle cross-sections for use in research studies and diagnostics. Public Library of Science 2020-12-23 /pmc/articles/PMC7757813/ /pubmed/33362264 http://dx.doi.org/10.1371/journal.pone.0243163 Text en © 2020 Stevens et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Stevens, Courtney R. Berenson, Josh Sledziona, Michael Moore, Timothy P. Dong, Lynn Cheetham, Jonathan Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle |
title | Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle |
title_full | Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle |
title_fullStr | Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle |
title_full_unstemmed | Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle |
title_short | Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle |
title_sort | approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7757813/ https://www.ncbi.nlm.nih.gov/pubmed/33362264 http://dx.doi.org/10.1371/journal.pone.0243163 |
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