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Life sciences · Journal article
Computers in Biology and Medicine · September 22, 2026
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Muscle mass and muscle quality, characterized by intramuscular adiposity and fiber composition, are clinically established biomarkers significantly associated with obesity, sarcopenia, frailty and cardiometabolic disorders. Current muscle quantification efforts are predominantly based on CT, often focusing on single anatomical region such as the L3 vertebral level, due it its relatively standardized acquisition protocols and the overall simplicity of developing muscle segmentation algorithms. However, MRI offers superior soft-tissue contrast for visualizing intramuscular fat and enables muscle assessment across a wider range of body regions. In this study, we developed SegmentAnyMuscle, a model capable of segmenting muscles across diverse anatomical regions and various MRI sequences, addressing the barriers to clinical muscle assessment. By developing upon a robust, expert-curated dataset of 316 MRI exams from 160 patients covering 11 anatomical regions, SegmentAnyMuscle demonstrated high segmentation performance with an averaged Dice Similarity Coefficient (DSC) of 88.45% on routinely acquired MRI sequences and maintained strong performance (DSC of 86.21%) under clinical challenging cases, including less common sequences, abnormalities such as muscular atrophy, implant-associated artifacts, and significant noise. Furthermore, automatic skeletal muscle mass measurement provided by SegmentAnyMuscle showed excellent correlation with expert manual assessments (Pearson correlation coefficient >0.99, p < 0.001). By providing the first reliable, automatic muscle segmentation tool across diverse anatomical regions and MRI acquisition settings, SegmentAnyMuscle can open new opportunities for MRI-based muscle health assessment and encourages broader indicators of metabolic and functional health. We made codes and model publicly available at: https://github.com/mazurowski-lab/SegmentAnyMuscle.