SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866916814331576320 |
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| author | Colglazier, Roy Lee, Jisoo Dong, Haoyu Gu, Hanxue Chen, Yaqian Cao, Joseph Yildiz, Zafer Liu, Zhonghao Konz, Nicholas Yang, Jichen Zhang, Jikai Chen, Yuwen Li, Lin Camarena, Adrian Mazurowski, Maciej A. |
| author_facet | Colglazier, Roy Lee, Jisoo Dong, Haoyu Gu, Hanxue Chen, Yaqian Cao, Joseph Yildiz, Zafer Liu, Zhonghao Konz, Nicholas Yang, Jichen Zhang, Jikai Chen, Yuwen Li, Lin Camarena, Adrian Mazurowski, Maciej A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22467 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI Colglazier, Roy Lee, Jisoo Dong, Haoyu Gu, Hanxue Chen, Yaqian Cao, Joseph Yildiz, Zafer Liu, Zhonghao Konz, Nicholas Yang, Jichen Zhang, Jikai Chen, Yuwen Li, Lin Camarena, Adrian Mazurowski, Maciej A. Signal Processing Computer Vision and Pattern Recognition 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. |
| title | SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI |
| topic | Signal Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.22467 |