"Bioimage analysis in Python" workshop at LMA2021
BSD-3-CLAUSE License
Repository for the Bioimage Analysis in Python tutorial at LMA 2021
In this tutorial, we will explore some of the most critical Python libraries for scientific computing on images, by walking through fundamental bioimage analysis applications of linear filtering (aka convolutions), segmentation, and object measurement, leveraging the napari viewer for interactive visualisation and processing. We will also demonstrate how to extend these concepts to bigger-than-RAM images using Dask.
The target audience are people aiming to work with images and doing image visualization and analysis. Intermediate Python experience (comfortable with python functions, classes, and running code in jupyter notebooks), experience with the scientific Python ecosystem (e.g. NumPy and SciPy) are desired tutorial prerequisites.
We recommend that you use Python 3.9 for this tutorial. Both 3.7 and 3.8 should also work but have not been tested. If you are using macOS Big Sur, only Python 3.9 works.
To perform this tutorial, we first need to set up our environment. To do so, please copy the repository containing the tutorial materials to your computer. We recommend cloning the materials into your Documents folder, but you can choose another suitable location.
If you already use git, follow these instructions to clone the repo to your local computer. You may want to change the folder to which you clone the repo.
cd ~/Documents
and then clone the repository. This will download all of the files necessary for this tutorial.
git clone https://github.com/jni/lma-2021-bioimage-analysis-python
Then, navigate to the directory you just cloned.
cd lma-2021-bioimage-analysis-python
If you don't want to use git, you can download a zip file from the repo:
Unzip the files to a folder of your choice.
Next we must install the dependencies for this tutorial, which can be done either with conda or pip.
We have provided a conda environment file to set up your python environment for this tutorial. To install the dependencies, please enter the following. This may take 5-10 minutes.
Use:
conda env create -f environment.yml
Follow the instructions for installation. When the installation completes successfully, you should see the following
done
#
# To activate this environment, use
#
# $ conda activate lma21
#
# To deactivate an active environment, use
#
# $ conda deactivate
Once the installation has been completed, activate your tutorial environment
conda activate lma21
Alternatively, in an environment including pip, use:
pip install -U -r requirements.txt
You can test to make sure napari was installed correctly launching napari from the command line using the command below.
napari --info
You are now ready to start the tutorial! We will perform the analysis using Jupyter Notebook. To start Jupyter Notebook, enter
jupyter notebook
Jupyter Notebook will open in a browser window and if you click on the lectures folder you'll be ready to get started!
For the dask tutorial, we are going to be using some 3D + t datasets from the Cell Tracking Challenge, specifically:
There are several forums you can use to get help and advice with your image analysis after this workshop is over.
As suggested by workshop participants