Image Analysis in Python#


This half-day course will introduce the use of Python packages to analyse and visualise image data common to neuroscience.

Course Summary:#

  • Interactive workflows in Python:

    • IPython for efficient data exploration

  • Basic image analysis techniques:

    • Utilizing NumPy, and scikit-image libraries

    • Visualization and exploration with napari

  • Processing large datasets effectively:

    • Introduction to Dask for parallel computing

    • Demonstration using BrainSaw data

  • Tools for processing histology data:

    • BrainGlobe tools for image registration and segmentation


If time allows, we will also look at using convolutional neural networks for tricky segmentation problems.

In advance of the course#

Installing packages#

Before attending the course, please install conda if you don’t already have it installed, and then run the following to install all relevant packages:

conda create --name image-analysis-python python=3.10 nb_conda_kernels -y
conda activate image-analysis-python
git clone
cd image-analysis-python
pip install -r requirements.txt

Download data#

To speed things up on the day, you may wish to download the data in advance. To do this:

  • Start jupyter lab (jupyter lab)

  • Open up the first notebook (e.g. notebooks/skimage_napari)

  • Set the conda environment (should be image-analysis-python based on the above commands)

  • Run the first code cell (the one that says Run the following cell to download the data in advance above it!)

  • Repeat for the other notebooks


The dask_cellfinder notebook has the largest sample data, so this is probably the best one to download in advance. Even on a fast (e.g. UCL) network, it may take ~1hr to download.