MyoSight—semi-automated image analysis of skeletal muscle cross sections

Babcock, Lyle; Hanna, Amy D.; Agha, Nadia H.; Hamilton, Susan L.

doi:10.1186/s13395-020-00250-5

Cited by 30 publications

(27 citation statements)

References 16 publications

(40 reference statements)

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“…Images of muscle sections were acquired with a 10× objective and analyzed using FIJI. Quantitation of fiber type and cross‐sectional area was semi‐automated using MyoSight 30 …”

Section: Methodsmentioning

confidence: 99%

Pathological mechanisms of vacuolar aggregate myopathy arising from a Casq1 mutation

et al. 2021

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Vacuolar aggregate myopathy (VAM, OMIM# 616231) is a rare human muscle disorder, linked to mutations in CASQ1 and presents with elevated serum creatine kinase, fatigue, myalgia, muscle cramps, lower limb hypertrophy, and muscle weakness. [1][2][3][4] Muscles from VAM patients frequently contain protein aggregates and an increased number of vacuoles. [1][2][3][4] Mutation of a conserved aspartic acid at position D244 to glycine (D244G, DG) in CASQ1 associated with VAM in humans slows sarcoplasmic reticulum (SR) Ca 2+ release and impairs polymerization of CASQ1. [1][2][3]5 Although the age at which VAM is diagnosed is variable, adults in their sixth decade are the most likely to experience muscle weakness and exercise intolerance. 1,3 Vacuoles and protein aggregates form mainly in Type II muscle fibers which stain positive for both the mutated protein and several non-mutated proteins, 1-4 suggesting the mutated proteins cause misfolding and aggregation of normal proteins. 6

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“…Images of muscle sections were acquired with a 10× objective and analyzed using FIJI. Quantitation of fiber type and cross‐sectional area was semi‐automated using MyoSight 30 …”

Section: Methodsmentioning

confidence: 99%

Pathological mechanisms of vacuolar aggregate myopathy arising from a Casq1 mutation

et al. 2021

Self Cite

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“…Several other semi and fully-automated softwares have been developed 4 – 8 , 22 – 29 . Among them, we have attempt to test and compare Open-CSAM, MuscleJ, SMASH, and MyoVision with MyoView and found MyoView is relatively easy to implement and more accurate for CSA quantification, especially in post-exercise regenerating muscles.…”

Section: Discussionmentioning

confidence: 99%

Automated image segmentation method to analyse skeletal muscle cross section in exercise-induced regenerating myofibers

Rahmati

Rashno

2021

Sci Rep

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Skeletal muscle is an adaptive tissue with the ability to regenerate in response to exercise training. Cross-sectional area (CSA) quantification, as a main parameter to assess muscle regeneration capability, is highly tedious and time-consuming, necessitating an accurate and automated approach to analysis. Although several excellent programs are available to automate analysis of muscle histology, they fail to efficiently and accurately measure CSA in regenerating myofibers in response to exercise training. Here, we have developed a novel fully-automated image segmentation method based on neutrosophic set algorithms to analyse whole skeletal muscle cross sections in exercise-induced regenerating myofibers, referred as MyoView, designed to obtain accurate fiber size and distribution measurements. MyoView provides relatively efficient, accurate, and reliable measurements for CSA quantification and detecting different myofibers, myonuclei and satellite cells in response to the post-exercise regenerating process. We showed that MyoView is comparable with manual quantification. We also showed that MyoView is more accurate and efficient to measure CSA in post-exercise regenerating myofibers as compared with Open-CSAM, MuscleJ, SMASH and MyoVision. Furthermore, we demonstrated that to obtain an accurate CSA quantification of exercise-induced regenerating myofibers, whole muscle cross-section analysis is an essential part, especially for the measurement of different fiber-types. We present MyoView as a new tool to quantify CSA, myonuclei and satellite cells in skeletal muscle from any experimental condition including exercise-induced regenerating myofibers.

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“…Several other semi and fully-automated softwares have been developed 4-8,18-25 . Among them, we have attempt to test and compare Open-CSAM, MuscleJ, SMASH, and MyoVision with MyoView and found MyoView is relatively easy to implement and more accurate for CSA quantification, especially in post-exercise regenerating muscles.…”

Section: Discussionmentioning

confidence: 99%

MyoView: Fully-automated image segmentation method to analyse skeletal muscle cross section in exercise-induced regenerating myofibers

Rahmati

Rashno

2021

Preprint

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Skeletal muscle is an adaptive tissue with the ability to regenerate in response to exercise training. Cross-sectional area (CSA) quantification, as a main parameter to assess muscle regeneration capability, is highly tedious and time-consuming, necessitating an accurate and automated approach to analysis. Although several excellent programs are available to automate analysis of muscle histology, they fail to efficiently and accurately measure CSA in regenerating myofibers in response to exercise training. Here, we have developed a novel fully-automated image segmentation method based on neutrosophic set algorithms to analyse whole skeletal muscle cross sections in exercise-induced regenerating myofibers, referred as MyoView, designed to obtain accurate fiber size and distribution measurements. MyoView provides relatively efficient, accurate, and reliable measurements for detecting different myofibers and CSA quantification in response to the post-exercise regenerating process. We showed that MyoView is comparable with manual quantification. We also showed that MyoView is more accurate and efficient to measure CSA in post-exercise regenerating myofibers as compared with Open-CSAM, MuscleJ, SMASH and MyoVision. Furthermore, we demonstrated that to obtain an accurate CSA quantification of exercise-induced regenerating myofibers, whole muscle cross-section analysis is an essential part, especially for the measurement of different fiber-types. We present MyoView as a new tool for CSA quantification in skeletal muscle from any experimental condition including exercise-induced regenerating myofibers.

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MyoSight—semi-automated image analysis of skeletal muscle cross sections

Cited by 30 publications

References 16 publications

Pathological mechanisms of vacuolar aggregate myopathy arising from a Casq1 mutation

Pathological mechanisms of vacuolar aggregate myopathy arising from a Casq1 mutation

Automated image segmentation method to analyse skeletal muscle cross section in exercise-induced regenerating myofibers

MyoView: Fully-automated image segmentation method to analyse skeletal muscle cross section in exercise-induced regenerating myofibers

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