| title | emoji | colorFrom | colorTo | sdk | sdk_version | app_file | license | python |
|---|---|---|---|---|---|---|---|---|
MyoQuant Streamlit |
🔬 |
yellow |
purple |
streamlit |
1.19.0 |
Home.py |
agpl-3.0 |
3.10.9 |
Please refer to the MyoQuant GitHub Repository or the MyoQuant PyPi repository for full documentation.
MyoQuant-Streamlit🔬 is a demo web interface to showcase the usage of MyoQuant.
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.
This web application is intended for demonstration purposes only.
The demo version is deployed at https://lbgi.fr/MyoQuant/. You can deploy your own demo version using Docker, your own python environment or google Colab for GPU support.
You can build the docker image by running docker build -t streamlit . and launch the container using docker run -p 8501:8501 streamlit.
If you do not want to use Docker you can install the poetry package in a miniconda (python 3.9, 3.10) base env, run poetry install to install the python env, activate the env with poetry shell and launch the app by running streamlit run Home.py.
As this application uses various deep-learning model, you could benefit from using a deployment solution that provides a GPU.
To do so, you can leverage Google Colab free GPU to boost this Streamlit application.
To run this app on Google Colab, simply clone the notebook called google_colab_deploy.ipynb into Colab and run the four cells. It will automatically download the latest code version, install dependencies and run the app. A link will appear in the output of the lat cell with a structure like https://word1-word2-try-01-234-567-890.loca.lt. Click it and the click continue and you’re ready to use the app!
Once on the demo, click on the corresponding staining analysis on the sidebar, and upload your histology image. Results will be displayed in the main area automatically.
For all analysis you can press the "Load Default File" to load a sample image to try the tool.
If you have an error like libcublas.so[0-9] cannot be found
It probably means that there is a weird CUDA on CPU-only hardware installation error. Try pip remove torch and pip install torch --index-url https://download.pytorch.org/whl/cpu, in your python virtual env. It should do the trick.
Creator and Maintainer: Corentin Meyer, 3rd year PhD Student in the CSTB Team, ICube — CNRS — Unistra [email protected]
The source code for MyoQuant is available HERE, for the demo website it is available HERE
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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