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
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
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).