MyoPath: A deep learning pipeline for objective morphometric assessment of skeletal muscle biopsies

Muscle biopsy assessment relies on descriptive features that are difficult to compare across samples. We developed MyoPath to quantify these features and examined their usefulness in clinical biopsies and population-based autopsy material.

MyoPath combines Cellpose-SAM and conventional image analysis within QuPath to segment myofibers, fat, nuclei, and connective tissue. We analyzed 478 hematoxylin and eosin images from HuashanMuscle (79 snap-frozen biopsies) and GTEx (399 formalin-fixed, paraffin-embedded autopsy samples). Thirty-seven features were summarized by seven pathology indicators. A composite model trained on GTEx healthy and wasting strata was transferred to HuashanMuscle without retraining.

Segmentation Dice coefficients ranged from 0.87 to 0.95 on annotated cryosections. Nuclear centralization index (NCI) and fiber size coefficient of variation (fiber CV) discriminated myopathy from controls in HuashanMuscle (AUC 0.85 and 0.79) and wasting from healthy donors in GTEx (0.74 and 0.74). Their GTEx associations changed little after age and sex adjustment. The composite achieved AUC 0.788 (95% CI 0.71-0.85) in GTEx and 0.873 (0.78-0.94) in HuashanMuscle. In the latter cohort, mean predicted probability was 0.49 compared with an observed myopathy proportion of 0.79.

NCI and fiber CV provide interpretable measures of muscle pathology in two distinct tissue settings. MyoPath supports quantitative comparison between groups; clinical severity grading and interpretation of absolute scores require further validation.