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SimpleITK Notebooks

SimpleITK Jupyter Notebooks for Education and Research

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SimpleITK Notebooks

SimpleITK is an abstraction layer and wrapper around the Insight Segmentation and Registration Toolkit (ITK). It is available in the following programming languages: C++, Python, R, Java, C#, Lua, Tcl and Ruby.

This repository contains a collection of Jupyter Notebooks illustrating the use of SimpleITK for educational and research activities. The notebooks demonstrate the use of SimpleITK for interactive image analysis using the Python and R programming languages.

The repository and its contents can be used for:

  1. Learning SimpleITK.
  2. As a basis for your teaching activities.
  3. As a basis for your research activities.

For the latter two use cases you can take advantage of the the repository's infrastructure which supports remote data downloads and notebook testing. These readily facilitate collaborative research.

The animation below is a visualization of a rigid CT/MR registration process created with SimpleITK and Python. The script used to generate the frames for the animated gif can be found in the repository's Utilities directory.

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Python

R

Try it Out

Thanks to the awesome people from the Binder Project you can try out the Python notebooks without installing a thing.

Some caveats:

  1. This free service is currently in beta, so may not always be available.
  2. Some of our notebooks require significant computational resources which may not be available.
  3. All cells that use the sitk.Show() command will generate an exception because they require a Fiji installation. Either ignore this or modify the code for the session.

After you launch binder, go to the Python directory and select the notebook of interest:

Binder

How to Cite

If you find these notebooks or the notebook testing infrastructure useful in your research, support our efforts by citing it as:

Z. Yaniv, B. C. Lowekamp, H. J. Johnson, R. Beare, "SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research", J Digit Imaging., https://doi.org/10.1007/s10278-017-0037-8, 2017.

If you are interested in the details, you can freely read the paper here.