SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI

TL;DR

This paper introduces a deep learning model for automated muscle segmentation in MRI scans, demonstrating high accuracy across various anatomical locations and imaging sequences, and publicly releasing the tool for research use.

Contribution

The study presents a novel, publicly available deep learning model capable of accurately segmenting muscles in MRI scans across different locations and sequences.

Findings

Achieved an average DSC of 88.45% on common sequences.

Achieved an average DSC of 86.21% on less frequent sequences and abnormal cases.

Demonstrated the model's applicability across diverse MRI settings.

Abstract

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.