MyoQuant: An optimized image analysis algorithm for quantitative analysis of skeletal muscle fibers

Muscle fiber cross-sectional area and type are widely used indicators of tissue health and function. Traditionally, these measurements are obtained through manual image analysis, a process that is labor-intensive, time-consuming, and prone to user variability. The presence of injury-related changes, such as fiber regeneration, scar tissue, and new vessel formation, further challenges the accuracy of existing automated segmentation tools. Additionally, artifacts introduced during tissue sectioning and immunostaining can interfere with the recognition and quantification of fibers. The purpose of this study is to develop a highly automated image analysis algorithm for quantifying muscle fiber morphology from full-slide histological images, which can exceed 1.3 GB in size. Our algorithm employs morphological transformations rather than traditional linear filtering to facilitate robust segmentation of individual muscle fibers. It simultaneously quantifies cellular morphological parameters, including cross-sectional area, orientation, and circularity. By combining immunofluorescent staining and color histograms, the algorithm also supports the classification of fibers based on histological markers. We developed and validated this tool to streamline large-scale muscle fiber analysis while minimizing the need for manual correction. Therefore, the image analysis algorithm described in this study provides a valuable tool for improving accuracy, objectivity, and proficiency in quantifying muscle fiber morphology.•The algorithm provides rapid and objective measurements of fiber morphology.•It handles complex tissue features and imaging artifacts.•It is flexible and efficient, improving reproducibility in muscle histology studies.