Automated Muscle and Fat Segmentation in Computed Tomography for Comprehensive Body Composition Analysis
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917096259059712 |
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| author | Chen, Yaqian Gu, Hanxue Chen, Yuwen Yang, Jichen Dong, Haoyu Cao, Joseph Y. Camarena, Adrian Mantyh, Christopher Colglazier, Roy Mazurowski, Maciej A. |
| author_facet | Chen, Yaqian Gu, Hanxue Chen, Yuwen Yang, Jichen Dong, Haoyu Cao, Joseph Y. Camarena, Adrian Mantyh, Christopher Colglazier, Roy Mazurowski, Maciej A. |
| contents | Body composition assessment using CT images can potentially be used for a number of clinical applications, including the prognostication of cardiovascular outcomes, evaluation of metabolic health, monitoring of disease progression, assessment of nutritional status, prediction of treatment response in oncology, and risk stratification for surgical and critical care outcomes. While multiple groups have developed in-house segmentation tools for this analysis, there are very limited publicly available tools that could be consistently used across different applications. To mitigate this gap, we present a publicly accessible, end-to-end segmentation and feature calculation model specifically for CT body composition analysis. Our model performs segmentation of skeletal muscle, subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) across the chest, abdomen, and pelvis area in axial CT images. It also provides various body composition metrics, including muscle density, visceral-to-subcutaneous fat (VAT/SAT) ratio, muscle area/volume, and skeletal muscle index (SMI), supporting both 2D and 3D assessments. To evaluate the model, the segmentation was applied to both internal and external datasets, with body composition metrics analyzed across different age, sex, and race groups. The model achieved high dice coefficients on both internal and external datasets, exceeding 89% for skeletal muscle, SAT, and VAT segmentation. The model outperforms the benchmark by 2.10% on skeletal muscle and 8.6% on SAT compared to the manual annotations given by the publicly available dataset. Body composition metrics show mean relative absolute errors (MRAEs) under 10% for all measures. Our model with weights is publicly available at https://github.com/mazurowski-lab/CT-Muscle-and-Fat-Segmentation.git. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09779 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Muscle and Fat Segmentation in Computed Tomography for Comprehensive Body Composition Analysis Chen, Yaqian Gu, Hanxue Chen, Yuwen Yang, Jichen Dong, Haoyu Cao, Joseph Y. Camarena, Adrian Mantyh, Christopher Colglazier, Roy Mazurowski, Maciej A. Image and Video Processing Computer Vision and Pattern Recognition Body composition assessment using CT images can potentially be used for a number of clinical applications, including the prognostication of cardiovascular outcomes, evaluation of metabolic health, monitoring of disease progression, assessment of nutritional status, prediction of treatment response in oncology, and risk stratification for surgical and critical care outcomes. While multiple groups have developed in-house segmentation tools for this analysis, there are very limited publicly available tools that could be consistently used across different applications. To mitigate this gap, we present a publicly accessible, end-to-end segmentation and feature calculation model specifically for CT body composition analysis. Our model performs segmentation of skeletal muscle, subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) across the chest, abdomen, and pelvis area in axial CT images. It also provides various body composition metrics, including muscle density, visceral-to-subcutaneous fat (VAT/SAT) ratio, muscle area/volume, and skeletal muscle index (SMI), supporting both 2D and 3D assessments. To evaluate the model, the segmentation was applied to both internal and external datasets, with body composition metrics analyzed across different age, sex, and race groups. The model achieved high dice coefficients on both internal and external datasets, exceeding 89% for skeletal muscle, SAT, and VAT segmentation. The model outperforms the benchmark by 2.10% on skeletal muscle and 8.6% on SAT compared to the manual annotations given by the publicly available dataset. Body composition metrics show mean relative absolute errors (MRAEs) under 10% for all measures. Our model with weights is publicly available at https://github.com/mazurowski-lab/CT-Muscle-and-Fat-Segmentation.git. |
| title | Automated Muscle and Fat Segmentation in Computed Tomography for Comprehensive Body Composition Analysis |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.09779 |