SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI
This enables consistent, reproducible research into musculature-health relationships, though it is incremental as it applies existing deep learning methods to a new medical imaging domain.
The study developed a publicly available deep learning model for automated muscle segmentation in MRIs across various anatomical locations and imaging sequences, achieving Dice Similarity Coefficients of 88.45% on common sequences and 86.21% on challenging cases with abnormalities.
The quantity and quality of muscles are increasingly recognized as important predictors of health outcomes. While MRI offers a valuable modality for such assessments, obtaining precise quantitative measurements of musculature remains challenging. This study aimed to develop a publicly available model for muscle segmentation in MRIs and demonstrate its applicability across various anatomical locations and imaging sequences. A total of 362 MRIs from 160 patients at a single tertiary center (Duke University Health System, 2016-2020) were included, with 316 MRIs from 114 patients used for model development. The model was tested on two separate sets: one with 28 MRIs representing common sequence types, achieving an average Dice Similarity Coefficient (DSC) of 88.45%, and another with 18 MRIs featuring less frequent sequences and abnormalities such as muscular atrophy, hardware, and significant noise, achieving 86.21% DSC. These results demonstrate the feasibility of a fully automated deep learning algorithm for segmenting muscles on MRI across diverse settings. The public release of this model enables consistent, reproducible research into the relationship between musculature and health.