MyoQuant🔬 is a command-line tool to automatically quantify pathological features in muscle fiber histology images.
It is built using CellPose, Stardist, custom neural-network models and image analysis techniques to automatically analyze myopathy histology images.
Currently MyoQuant is capable of quantifying centralization of nuclei in muscle fiber with HE staining, anomaly in the mitochondria distribution in muscle fibers with SDH staining and the number of type 1 muscle fiber vs type 2 muscle fiber with ATP staining.

An online demo with a web interface is available at https://lbgi.fr/MyoQuant/. This project is free and open-source under the AGPL license, feel free to fork and contribute to the development.

MyoQuant package is officially available on PyPi (pip) repository. https://pypi.org/project/myoquant/

Using pip, you can simply install MyoQuant in a python environment with a simple: pip install myoquant

1. Clone this repository using git clone https://github.com/lambda-science/MyoQuant.git
2. Create a virtual environment by using python -m venv .venv
3. Activate the venv by using source .venv/bin/activate
4. Install MyoQuant by using pip install -e .

To use the command-line tool, first activate your venv in which MyoQuant is installed: source .venv/bin/activate
Then you can perform SDH or HE analysis. You can use the command myoquant --help to list available commands.

• For SDH Image Analysis the command is:
  myoquant sdh-analysis IMAGE_PATH
  Don't forget to run myoquant sdh-analysis --help for information about options.
• For HE Image Analysis the command is:
  myoquant he-analysis IMAGE_PATH
  Don't forget to run myoquant he-analysis --help for information about options.
• For ATP Image Analysis the command is:
  myoquant atp-analysis IMAGE_PATH
  Don't forget to run myoquant atp-analysis --help for information about options.

If you're running into an issue such as myoquant: command not found please check if you activated your virtual environment with the package installed. And also you can try to run it with the full command: python -m myoquant sdh-analysis --help

Creator and Maintainer: Corentin Meyer, PhD in Biomedical AI [email protected]

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For HE Staining analysis, you can download this sample image: HERE
For SDH Staining analysis, you can download this sample image: HERE
For ATP Staining analysis, you can download this sample image: HERE

1. Example of successful SDH analysis output with: myoquant sdh-analysis sample_sdh.jpg

2. Example of HE analysis: myoquant he-analysis sample_he.jpg

3. Example of ATP analysis with: myoquan atp-analysis sample_atp.jpg

For the SDH Analysis our custom model will be downloaded and placed inside the myoquant package directory. You can also download it manually here: https://lbgi.fr/~meyer/SDH_models/model.h5 and then you can place it in the directory of your choice and provide the path to the model file using:
myoquant sdh-analysis IMAGE_PATH --model_path /path/to/model.h5

In a effort to push for open-science, MyoQuant SDH dataset and model and availiable on HuggingFace🤗

MyoQuant is born within the collaboration between the CSTB Team @ ICube led by Julie D. Thompson, the Morphological Unit of the Institute of Myology of Paris led by Teresinha Evangelista, the imagery platform MyoImage of Center of Research in Myology led by Bruno Cadot, the photonic microscopy platform of the IGMBC led by Bertrand Vernay and the Pathophysiology of neuromuscular diseases team @ IGBMC led by Jocelyn Laporte