Body sizes and shapes vary widely, even among healthy adults, resulting in diverse muscle sizes, strengths, and performance capacities. This study developed an AI (artificially intelligent) algorithm to segment individual muscles and bones from whole-body MRI scans of 102 healthy adults (49 males, 53 females) aged 18–50 years, generating 3D segmentations of 70 muscles and 13 bones spanning the upper limbs, trunk, and lower limbs. We quantified muscle volume, asymmetry, and fat fraction at whole-body, regional, and individual-muscle levels, and examined how these properties correlate with body size and skeletal dimensions. Fat fraction and asymmetry varied across muscles and were generally similar between sexes; however, distribution of muscle volume across the body differed between females and males. Across all predictors tested, total bone volume showed the strongest correlation with total muscle volume (r² = 0.85), followed by femur volume, height × mass, mass, height, and BMI. At the individual muscle level, the associated bone volume consistently explained more variance in muscle size than anthropometric predictors. Correlations between muscle volume and body-size parameters were significantly different between males and females, while bone-volume correlations showed no significant sex differences. These results suggest that skeletal dimensions—reflecting an individual’s “frame size”—are stronger determinants of muscularity than body size metrics and explain the observed sex differences in muscle sizes. This work presents a comprehensive in vivo muscle-level dataset to date, introduces a novel framework for analyzing muscle–bone correlations, and provides reference data for applications from clinical diagnostics to athletic performance and musculoskeletal modeling.
Pabon et al. (2025) studied this question.