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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)**. |