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.

View Static Converted Pages

Thanks to the binder service, notebooks can be run in the cloud without any local installation.

If you want to interact with all of the notebooks, then just click this button:

If you are just browsing the static rendering, most of the notebooks provide the option to launch binder for the particular notebook. You will find a "launch binder" button at the top right of the notebook. Clicking it will, well, launch binder and allow you to interactively run and modify the notebook using the service without any local installation.

Python

• 00 Setup
• 01 Image Basics
• 02 Pythonic Image
• 03 Image Details
• 04 Image Display
• 05 Results Visualization
• 10 matplotlib's imshow
• 11 Progress
• 20 Expand With Interpolators
• 21 Transforms and Resampling
• 22 Transforms

• 300 Segmentation Overview
• 30 Segmentation Region Growing
• 31 Levelset Segmentation
• 32 Watersheds Segmentation
• 33 Segmentation Thresholding Edge Detection
• 34 Segmentation Evaluation
• 35 Segmentation Shape Analysis
• 36 Microscopy Colocalization Distance Analysis

• 60 Registration Introduction
• 61 Registration Introduction Continued
• 62 Registration Tuning
• 63 Registration Initialization
• 64 Registration Memory Time Tradeoff
• 65 Registration FFD
• 66 Registration Demons
• 67 Registration Semiautomatic Homework
• 68 Registration Errors (FLE, FRE, TRE)
• 69 x-ray panorama

• 51 VH Segmentation1
• 55 VH Resample
• 56 VH Registration1
• 70 Data Augmentation
• 71 Trust But Verify

R

• 00 Setup
• Image Basics
• R style image
• 22 Transforms

• 300 Segmentation Overview
• 33 Segmentation Thresholding Edge Detection
• 34 Segmentation Evaluation
• 35 Cell Segmentation

• 60 Registration Introduction
• 61 Registration Introduction Continued
• 65 Registration FFD
• 66 Registration Demons

• 70 Data Augmentation

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., 31(3): 290-303, 2018, https://doi.org/10.1007/s10278-017-0037-8.