---
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)**.