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

Area of Science:

• Exercise physiology
• Muscle biology
• Biomedical imaging analysis

Background:

• Skeletal muscle regeneration is crucial for recovery after exercise.
• Accurate quantification of muscle fiber cross-sectional area (CSA) is vital for assessing regeneration.
• Existing automated methods struggle with CSA measurement in regenerating myofibers.

Purpose of the Study:

• To develop a fully-automated image segmentation method for analyzing skeletal muscle cross-sections.
• To accurately measure CSA, fiber distribution, myonuclei, and satellite cells in exercise-induced regenerating myofibers.
• To present MyoView as a novel tool for skeletal muscle analysis.

Main Methods:

• Development of a novel automated image segmentation method using neutrosophic set algorithms.
• Application of the MyoView tool to analyze whole skeletal muscle cross-sections.
• Comparison of MyoView's performance against manual quantification and other existing software (Open-CSAM, MuscleJ, SMASH, MyoVision).

Main Results:

• MyoView provides efficient, accurate, and reliable measurements for CSA quantification.
• The tool accurately detects different myofibers, myonuclei, and satellite cells in regenerating muscle.
• MyoView demonstrates comparable accuracy to manual quantification and superior performance over existing automated tools.

Conclusions:

• MyoView is a highly accurate and efficient tool for quantifying CSA in exercise-induced regenerating myofibers.
• Whole muscle cross-section analysis is essential for accurate CSA quantification, especially for different fiber types.
• MyoView serves as a valuable new tool for skeletal muscle research across various experimental conditions.