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Short Description
Open-source python package for the extraction of Radiomics features
Full Description

pyradiomics v1.1.1

Radiomics feature extraction in Python

This is an open-source python package for the extraction of Radiomics features from 2D and 3D images and
segmentations.

Image loading and preprocessing (e.g. resampling and cropping) are first done using SimpleITK.
Then, loaded data are converted into numpy arrays for further calculation using feature classes
outlined below.

With this package we aim to establish a reference standard for Radiomic Analysis, and provide a tested and mantained
open-source platform for easy and reproducible Radiomic Feature extraction.

By doing so, we hope to increase awareness of radiomic capabilities and expand the community.

If you publish any work which uses this package, please cite the following publication:\
Joost JM van Griethuysen, Andriy Fedorov, Chintan Parmar, Ahmed Hosny, Nicole Aucoin, Vivek Narayan, Regina GH
Beets-Tan, Jean-Christophe Fillion-Robin, Steve Pieper, Hugo JWL Aerts, “Computational Radiomics System to Decode the
Radiographic Phenotype”; Submitted 2017

Feature Classes

Currently supports the following feature classes:

  • First Order Statistics
  • Shape-based
  • Gray Level Cooccurence Matrix (GLCM)
  • Gray Level Run Length Matrix (GLRLM)
  • Gray Level Size Zone Matrix (GLSZM)

Filter Classes

Aside from the feature classes, there are also some built-in optional filters:

  • Laplacian of Gaussian (LoG, based on SimpleITK functionality)
  • Wavelet (using the PyWavelets package)
  • Square
  • Square Root
  • Logarithm
  • Exponential

Supporting reproducible extraction

Aside from calculating features, the pyradiomics package includes provenance information in the
output. This information contains information on used image and mask, as well as applied settings
and filters, thereby enabling fully reproducible feature extraction.

Documentation

For more information, see the sphinx generated documentation available here.

Alternatively, you can generate the documentation by checking out the master branch and running from the root directory:

python setup.py build_sphinx

The documentation can then be viewed in a browser by opening PACKAGE_ROOT\build\sphinx\html\index.html.

Furthermore, an instruction video is available here.

Installation

PyRadiomics is OS independent and compatible with both Python 2.7 and Python >=3.4.
To install this package on unix like systems run the following commands from the root directory:

sudo python -m pip install -r requirements.txt
sudo python setup.py install

Detailed installation instructions, as well as instructions for installing PyRadiomics on Windows are available in the
documentation.

Docker

PyRadiomics published a Dockers based off a Jupyter notebook. PyRadiomics is pre-installed with example Notebooks. To build the Docker:

docker build -t radiomics/notebook .

The radiomics/notebook Docker has an exposed volume (/data) that can be mapped to the host system directory. For example, to mount the current directory:

docker run --rm -it --publish 8888:8888 -v `pwd`:/data radiomics/notebook

or for a less secure notebook, skip the randomly generated token

docker run --rm -it --publish 8888:8888 -v `pwd`:/data radiomics/notebook start-notebook.sh --NotebookApp.token=''

and open the local webpage at http://localhost:8888/ with the current directory at http://localhost:8888/tree/data.

The Docker ships with two command line applications to compute features. Creative use of the Docker command line allows processing of image data in the local (host) directory:

docker run --rm -w $(pwd) -v $(pwd):$(pwd) radiomics/notebook pyradiomics data/brain1_image.nrrd data/brain1_label.nrrd

The -w argument sets the working directory inside the docker to $(pwd) (the current working directory), and -v maps the local directory into the same directory within the Docker. Running the command from the Pyradiomics checkout produces this:

docker run --rm -w $(pwd) -v $(pwd):$(pwd) radiomics/notebook pyradiomics data/brain1_image.nrrd data/brain1_label.nrrd

# output ...
general_info_BoundingBox: (162, 84, 11, 47, 70, 7)
general_info_GeneralSettings: {'normalize': False, 'enableCExtensions': True, 'distances': [1], 'interpolator': 'sitkBSpline', 'additionalInfo': True, 'label': 1, 'normalizeScale': 1, 'padDistance': 5, 'force2Ddimension': 0, 'removeOutliers': None, 'minimumROISize': None, 'minimumROIDimensions': 1, 'resampledPixelSpacing': None, 'force2D': False}
general_info_ImageHash: 5c9ce3ca174f0f8324aa4d277e0fef82dc5ac566
general_info_ImageSpacing: (0.7812499999999999, 0.7812499999999999, 6.499999999999998)
general_info_InputImages: {'Original': {}}
general_info_MaskHash: 9dc2c3137b31fd872997d92c9a92d5178126d9d3
general_info_Version: 0+unknown
general_info_VolumeNum: 2
general_info_VoxelNum: 4137
original_shape_SurfaceArea: 6438.821603779402
# ...

Usage

PyRadiomics can be easily used in a Python script through the featureextractor
module. Furthermore, PyRadiomics provides two commandline scripts, pyradiomics
and pyradiomicsbatch, for single image extraction and batchprocessing, respectively.
Finally, a convenient front-end interface is provided as the 'Radiomics'
extension for 3D Slicer, available here.

3rd-party packages used in pyradiomics:

  • SimpleITK
  • numpy
  • PyWavelets (Wavelet filter)
  • pykwalify (Enabling yaml parameters file checking)
  • tqdm (Progressbar)
  • six (Python 3 Compatibility)
  • sphinx (Generating documentation)
  • sphinx_rtd_theme (Template for documentation)
  • nose-parameterized (Testing)

See also the requirements file.

WIP

License

This package is covered by the open source 3D Slicer License.

Developers

<sup>1</sup>Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA,
<sup>2</sup>Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA,
<sup>3</sup>Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands,
<sup>4</sup>GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands,
<sup>5</sup>Kitware,
<sup>6</sup>Isomics

Contact

We are happy to help you with any questions. Please contact us on the pyradiomics email list.

We welcome contributions to PyRadiomics. Please read the contributing guidelines on how to contribute
to PyRadiomics.

This work was supported in part by the US National Cancer Institute grant
5U24CA194354, QUANTITATIVE RADIOMICS SYSTEM DECODING THE TUMOR PHENOTYPE.

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