Key result
A semi-automated image-analysis pipeline combining Fiji/ImageJ, CellProfiler, Cellpose, and Python provides a robust and efficient platform for high-throughput analysis of muscle tissue cross-sections.
The developed semi-automated pipeline provides a robust and efficient platform for high-throughput analysis of skeletal muscle tissue cross-sections.
May enable scalable muscle histology research; leaves open clinical validation before routine use.
Manual analysis of skeletal muscle cross-sections is time-consuming and subject to error and user bias. To overcome these limitations, we developed and validated a semi-automated, quantitative, and reproducible image-analysis pipeline specifically tailored to quantify Pax7+ satellite cells, myonuclei, and cross-sectional area by fiber type. The workflow combines Fiji/ImageJ-based image preprocessing with CellProfiler, Cellpose, and a custom Python script to process and analyze immunohistological images of muscle tissue cross-sections. Outcomes include Pax7+ satellite cells and myonuclei quantified per fiber by fiber type, along with cross-sectional area, perimeter, and fiber type classification. This semi-automated approach provides a robust and efficient platform for high-throughput analysis of muscle tissue cross-sections from large datasets.
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Megowan et al. (2026) studied Skeletal muscle tissue analysis. Semi-automated image-analysis pipeline vs. Manual analysis was evaluated on Quantitation of Pax7+ satellite cells, myonuclei, and cross-sectional area by fiber type. A semi-automated image-analysis pipeline combining Fiji/ImageJ, CellProfiler, Cellpose, and Python provides a robust and efficient platform for high-throughput analysis of muscle tissue cross-sections.
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