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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 |
Sumario: | 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. |
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