LegSegNet

GitHub: https://github.com/gogochen07/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/gogochen07/LegSegNet

Citation

Please cite the following manuscript if you find the model useful:

License

This project is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

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