| --- |
| license: cc-by-nc-4.0 |
| library_name: pytorch |
| tags: |
| - medical-image-segmentation |
| - ct |
| - lower-extremity |
| - body-composition |
| - computer-vision |
| pipeline_tag: image-segmentation |
| --- |
| |
| # LegSegNet |
|
|
| **GitHub:** [https://github.com/mazurowski-lab/LegSegNet](https://github.com/mazurowski-lab/LegSegNet) |
|
|
| **LegSegNet** is a deep learning system for lower extremity CT tissue segmentation and body composition quantification. |
|
|
| Given an input CT scan, LegSegNet segments four tissue compartments: **bone**, **skeletal muscle**, **subcutaneous adipose tissue (SAT)**, and **inter- and intramuscular adipose tissue (IMAT)**. |
|
|
| The system can further convert predicted masks into quantitative measurements, including tissue area, tissue volume, CT attenuation, and tissue-volume ratios, supporting downstream medical image analysis. |
|
|
| ## Model Details |
|
|
| - **Task:** Lower extremity CT tissue segmentation |
| - **Input:** Lower extremity CT images/volumes |
| - **Output:** Multi-class segmentation mask and body composition measurements |
| - **Labels:** Background, SAT, skeletal muscle, IAT, bone |
|
|
| ## Usage |
|
|
| Please refer to the GitHub repository for more details: |
|
|
| [https://github.com/mazurowski-lab/LegSegNet](https://github.com/mazurowski-lab/LegSegNet) |
|
|
| ## Citation |
|
|
| Please cite the following manuscript if you find the model useful: |
|
|
| ``` |
| @article{chen2026legsegnet, |
| title={LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification}, |
| author={Chen, Yuwen and Chen, Yaqian and Colglazier, Roy and Dong, Haoyu and Gu, Hanxue and Mazurowski, Maciej A and Southerland, Kevin W}, |
| journal={arXiv preprint arXiv:2605.30829}, |
| year={2026} |
| } |
| ``` |
|
|
| ## License |
|
|
| This project is released under the **Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)**. |
