Myo Vision: Software for automated high-content analysis of skeletal muscle immunohistochemistry

Yuan Wen, Kevin A. Murach, Ivan J. Vechetti, Christopher Fry, Chase Vickery, Charlotte A. Peterson, John J. McCarthy, Kenneth S. Campbell

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

Analysis of skeletal muscle cross sections is an important experimental technique in muscle biology. Many aspects of immunohistochemistry and fluorescence microscopy can now be automated, but most image quantification techniques still require extensive human input, slowing progress and introducing the possibility of user bias. MyoVision is a new software package that was developed to overcome these limitations. The software improves upon previously reported automatic techniques and analyzes images without requiring significant human input and correction. When compared with data derived by manual quantification, MyoVision achieves an accuracy of > 94% for basic measurements such as fiber number, fiber type distribution, fiber cross-sectional area, and myonuclear number. Scientists can download the software free from www.MyoVision.org and use it to automate the analysis of their own experimental data. This will improve the efficiency and consistency of the analysis of muscle cross sections and help to reduce the burden of routine image quantification in muscle biology.

Original languageEnglish (US)
Pages (from-to)40-51
Number of pages12
JournalJournal of Applied Physiology
Volume124
Issue number1
DOIs
StatePublished - Jan 1 2018

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Keywords

  • Automation software
  • Cell morphology
  • High-content microscopy
  • Image analysis
  • Skeletal muscle

ASJC Scopus subject areas

  • Physiology
  • Physiology (medical)

Cite this

Wen, Y., Murach, K. A., Vechetti, I. J., Fry, C., Vickery, C., Peterson, C. A., McCarthy, J. J., & Campbell, K. S. (2018). Myo Vision: Software for automated high-content analysis of skeletal muscle immunohistochemistry. Journal of Applied Physiology, 124(1), 40-51. https://doi.org/10.1152/japplphysiol.00762.2017