| --- |
| 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)**. |
