SimpleITK: image analysis for all levels of programming expertise
Speakers: Hans Johnson - Speaker Website
University of Iowa
Bradley C. Lowekamp - Speaker Website
National Institute of Allergy and Infectious Diseases, NIH
Ziv Yaniv - Speaker Website
National Institute of Allergy and Infectious Diseases, NIH
Abstract:
SimpleITK is a simplified programming interface to the algorithms and data structures of the Insight Toolkit (ITK) for segmentation, registration and advanced image analysis. It supports bindings for multiple programming languages including C++, Python, R, Java, C#, Lua, Ruby and TCL. Combining SimpleITK’s Python bindings with the Jupyter notebook web application creates an environment which facilitates collaborative development of biomedical image analysis workflows.
In this tutorial, we will use a hands-on approach utilizing Jupyter notebooks to explore and experiment with various SimpleITK features in the Python programming language. Participants will follow along using their personal laptops, enabling them to explore the effects of code changes and parameter settings not covered by the instructor. We will start with a short introduction to the toolkit’s two basic data elements, Images and Transformations. Combining the two classes we show how to use SimpleITK as a tool for image preparation and data augmentation for deep learning via spatial and intensity transformations. We will then present various features available in the toolkit’s registration framework and components for constructing a segmentation workflow. Finally, we will show how to use the toolkit for qualitative, visual, and quantitative evaluation of segmentation and registration results.