MyoPath: A Deep Learning Pipeline for Objective Morphometric Assessment of Skeletal Muscle Biopsies

Abstract Histopathological evaluation of skeletal muscle biopsies relies on subjective, semi-quantitative assessment with no standardized grading system. We developed a four-tissue deep learning segmentation pipeline using Cellpose-SAM for myofiber instance segmentation, a pixel classifier for fat infiltration, and watershed detection for nuclei. We applied this pipeline to 478 H&E whole-slide images from two independent cohorts: HuashanMuscle (n = 79; China; myotonic dystrophy type 1 [DM1], n = 28; limb-girdle muscular dystrophy type R1 [LGMDR1, calpainopathy], n = 12; type R2 [LGMDR2, dysferlinopathy], n = 22; controls, n = 17) and GTEx (n = 399; United States; three-level myopathy spectrum). Thirty-seven unique morphometric features were extracted per sample. Nuclear centralization index (NCI) and fiber size variability coefficient (fiber CV) discriminated myopathy from controls (p = 1.3 × 10 −5 , rank-biserial r = 0.69; and p = 2.9 × 10 −4 , r = 0.58, respectively). DM1 showed the highest NCI (median 0.121), consistent with its centronuclear pathology, and NCI correlated with CTG repeat count (Spearman rho = 0.46, p = 0.042, n = 20). In the GTEx cohort, both biomarkers exhibited significant dose-response trends across the myopathy spectrum (Jonckheere-Terpstra p < 10 −4 ). The MyoPath Score, a logistic regression composite of seven pathology indicators trained on GTEx, achieved AUC = 0.788 (LOO-CV 0.735) and transferred to the independent HuashanMuscle cohort with AUC = 0.873 without retraining. Segmentation achieved Dice coefficients of 0.92 (myofiber), 0.95 (fat), 0.87 (nucleus), and 0.88 (connective tissue), with intraclass correlation coefficients exceeding 0.88. NCI and fiber CV provide objective, reproducible quantitative biomarkers for skeletal muscle pathology severity assessment with potential as standardized grading criteria and clinical trial endpoints.