• Support material for the tutorial Introduction to scikit-image for 3D image analysis at the Computational Biology Skills Seminar.

This tutorial will introduce how to analyze three dimensional stacked and volumetric images in Python, mainly using scikit-image. Here we will study how to:

• pre-process data using filtering, binarization and segmentation techniques.
• inspect, count and measure attributes of objects and regions of interest in the data.
• visualize large 3D data.

For more info:

• [scikit-image]
• [Computational Biology Core Skills Seminars]
• [CompBio Skills Seminar]: webpage containing previous talks, slides and code

If you are new to Python, please install the Anaconda distribution for Python 3 (available on Linux, OSX, and Windows). If you have more experience, feel free to use your favorite distribution.

Please ensure the requirements below are met:

• numpy >= 1.22
• scipy >= 1.8
• matplotlib >= 3.5
• scikit-image >= 0.19
• jupyter-notebook >= 6.4
• itk >= 5.2
• itkwidgets >= 0.32

For more details, see "Test your setup" below.

Please create an environment ready for this tutorial using the YAML file provided. The command is:

conda env create --file=environment.yml

The environment created in the last step is called ccb_skimage, and you can start using it with the command conda activate:

conda activate ccb_skimage

To download the material for this tutorial, you can clone or download the repository at https://github.com/alexdesiqueira/ccb_skimage3d_tutorial.

You can use the script check_setup.py to check if your environment is ready.

python check_setup.py

On my computer, the command results in:

[✓] numpy            1.22.3
[✓] scipy            1.8.0
[✓] matplotlib       3.5.2
[✓] scikit-image     0.19.2
[✓] jupyter-notebook 6.4.11
[✓] itk              5.2.0
[✓] itkwidgets       0.32.0

Please note that your version numbers may differ.