---
library_name: pytorch
license: cc-by-nc-4.0
pipeline_tag: image-segmentation
tags:
- medical-image-segmentation
- ct
- lower-extremity
- body-composition
- computer-vision
---

# LegSegNet

LegSegNet is a deep learning system for lower extremity CT tissue segmentation and body composition quantification, introduced in the paper [LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification](https://huggingface.co/papers/2605.30829).

**GitHub:** [https://github.com/mazurowski-lab/LegSegNet](https://github.com/mazurowski-lab/LegSegNet)

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 (PNG) or volumes (NIfTI)
- **Output:** Multi-class segmentation mask and body composition measurements
- **Labels:** 
  - 0: Background
  - 1: SAT (Subcutaneous adipose tissue)
  - 2: Muscle (Skeletal muscle)
  - 3: Inter/Intra (Inter- and intramuscular adipose tissue)
  - 4: Bone

## Usage

LegSegNet uses a pretrained nnU-Net model. For a practical end-to-end workflow including a Gradio interface, please refer to the official GitHub repository.

### Installation and Inference

To run the application locally:

```bash
pip install -r requirements.txt
python app.py
```

### File Structure

Ensure the model files are organized as follows to use the inference scripts:
```text
model/
|-- plans.json
|-- dataset.json
|-- fold_0/
    |-- checkpoint_best.pth
```

## Citation

If you find LegSegNet useful, please cite the following manuscript:

```bibtex
@article{legsegnet2026,
  title={LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification},
  author={Gao, Chen and others},
  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)**.