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.

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)

WIP

Implementation of this package as an extension to 3D Slicer

License

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

Developers

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

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.

Marphy/pyradiomics