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Quantification of objects in histological slices

Project description

cuisto

Python package for histological quantification of objects in reference atlas regions.

cuisto uses data exported from QuPath used with ABBA to pool data and derive, average and display metrics.

Check the full documentation : https://teamncmc.github.io/cuisto

Install

Steps 1-3 below need to be performed only once. If Anaconda or conda is already installed, skip steps 1-2 and use the Anaconda prompt instead.

  1. Install Miniforge, as user, add conda to PATH and make it the default interpreter.
  2. Open a terminal (PowerShell in Windows). run : conda init and restart the terminal.
  3. Create a virtual environment named "hq" with Python 3.12:
    conda create -n hq python=3.12
    
  4. Activate the environment:
    conda activate hq
    
  5. Download the latest release from here (choose "Source code (zip)) and unzip it on your computer.
  6. Browse to the repository from the terminal and install the cuisto package:
    cd /path/to/the/unzipped/folder
    pip install .
    

The cuisto will be then available in Python from anywhere as long as the hq conda environment is activated. You can get started by looking and using the Jupyter notebooks.

For more complete installation instructions, see the documentation.

Using notebooks

Some Jupyter notebooks are available in the "docs/demo_notebooks" folder. You can open them in an IDE (such as vscode, select the "hq" environment as kernel in the top right) or in the Jupyter web interface (jupyter notebook in the terminal, with the "hq" environment activated).

Brain structures

You can generate brain structures outlines coordinates in three projections (coronal, sagittal, top-view) with the script in scripts/atlas/generate_atlas_outline.py. They are used to overlay brain regions outlines in 2D projection density maps. It might take a while so you can also grab a copy of those files here:

Build the doc

To build and look at the documentation offline : In step 5. above, replace the pip install . command with :

pip install .[doc]

Then, run :

mkdocs serve

Head to http://localhost:8000/ from a web browser. The documentation is built with MkDocs using the Material theme. KaTeX CSS and fonts are embedded instead of using a CDN, and are under a MIT license.

Credits

cuisto has been primarly developed by Guillaume Le Goc in Julien Bouvier's lab at NeuroPSI. The clever name was found by Aurélie Bodeau.

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