Kitware Course in Biomedical Image Analysis and Visualization: ITK
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.gitignore DOC: Start Segmentation May 29, 2018
0_Index.ipynb ENH: Move Index to 0_Index May 30, 2018
1_Introduction_to_the_Insight_Toolkit.ipynb
2_Image_Filtering.ipynb
3_Segmentation.ipynb ENH: Registration objectives May 30, 2018
4_ITK_and_NumPy.ipynb
4_ITK_and_NumPy_answers_Exercise1.ipynb Add ITK NumPy notebook May 30, 2018
4_ITK_and_NumPy_answers_Exercise2.ipynb Add ITK NumPy notebook May 30, 2018
5_Registration.ipynb
6_Extending_the_Toolkit.ipynb
6_Extending_the_toolkit_answers_Exercise1.ipynb
6_Extending_the_toolkit_answers_Exercise2.ipynb
Dockerfile.jupyterhub
Dockerfile.local BUG: Extending the Toolkit filename May 30, 2018
LICENSE
README.rst DOC: README grammar May 31, 2018
build.sh ENH: tag Docker image with latest Git revision May 7, 2018
check_env.ipynb ENH: Add check_env.ipynb May 29, 2018
run.sh BUG: Fix the docker image name in run.sh May 29, 2018

README.rst

Biomedical Image Analysis and Visualization: ITK

Kitware, Carrboro, North Carolina, USA

Instructors:

  • Matt McCormick, PhD
  • Dženan Zukić, PhD
  • Francois Budin

Kitware

ITK

The Insight Segmentation and Registration Toolkit (ITK) (www.itk.org) has become a standard in academia and industry for medical image analysis. In recent years, the ITK community has focused on providing programming interfaces to ITK from Python and JavaScript and making ITK available via leading applications such as Slicer and ImageJ. In this course we present best practices for taking advantage of ITK in your imaging research and commercial products. We demonstrate how script writing and can be used to access the algorithms in ITK and the multitude of ITK extensions that are freely available on the web.

Run the Tutorial

There are many ways to run these tutorials.

On the Web, with Binder

To run the notebooks in MyBinder, simply click this link.

Locally, with Docker

First, install Docker, if not already available.

Next, clone the repository:

git clone https://github.com/KitwareMedical/2018-05-30-KRSCourseInBiomedicalImageAnalysisAndVisualization.git
cd 2018-05-30-KRSCourseInBiomedicalImageAnalysisAndVisualization

Then, build and run the Docker image:

./build.sh
./run.sh

Paste the URL presented in the terminal in your web browser.

Locally, with Python from Python.org or a System Python

First, install Python, if not already available.

Next, install the required dependencies:

python -m pip install jupyter matplotlib numpy scipy ipywidgets scikit-learn
python -m jupyter nbextension enable --py widgetsnbextension
python -m pip install itkwidgets
python -m jupyter nbextension enable --py --sys-prefix itkwidgets
python -m pip install itk itk-texturefeatures itk-ultrasound

Then, clone the repository:

git clone https://github.com/KitwareMedical/2018-05-30-KRSCourseInBiomedicalImageAnalysisAndVisualization.git
cd 2018-05-30-KRSCourseInBiomedicalImageAnalysisAndVisualization

And start Jupyter:

python -m jupyter notebook

Locally, with Conda

First, install MiniConda or Anaconda, if not already available.

Next, install the required dependencies:

conda install -c conda-forge jupyter matplotlib numpy scipy ipywidgets scikit-learn
python -m pip install itkwidgets
python -m jupyter nbextension enable --py --sys-prefix itkwidgets
python -m pip install itk itk-texturefeatures itk-ultrasound

Then, clone the repository:

git clone https://github.com/KitwareMedical/2018-05-30-KRSCourseInBiomedicalImageAnalysisAndVisualization.git
cd 2018-05-30-KRSCourseInBiomedicalImageAnalysisAndVisualization

And start Jupyter:

python -m jupyter notebook