Welcome to pyradiomics documentation!¶
This is an open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks.
Image loading and preprocessing (e.g. resampling and cropping) are first done using
Then, loaded data are converted into numpy arrays for further calculation using feature classes
Currently supports the following feature 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 Root
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.
If you publish any work which uses this package, please cite the following publication:
Joost J.M. van Griethuysen et al, “Computational Radiomics System to Decode the Radiographic Phenotype”; Submitted
3rd-party packages used in pyradiomics¶
- PyWavelets (Wavelet filter)
- pykwalify (Enabling yaml parameters file checking)
- tqdm (Progressbar)
- sphinx (Generating documentation)
- sphinx_rtd_theme (Template for documentation)
- nose-parameterized (Testing)
- Clone the repository
git clone git://github.com/Radiomics/pyradiomics
- Install on your system, with prerequisites:
sudo python -m pip install -r requirements.txt
sudo python setup.py install
- For more detailed installation instructions see Installation Details
- radiomics package
This package is covered by the 3D Slicer License.
This work was supported in part by the US National Cancer Institute grant 5U24CA194354, QUANTITATIVE RADIOMICS SYSTEM DECODING THE TUMOR PHENOTYPE.
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
- HJWL Aerts, ER Velazquez, RTH Leijenaar, et al., “Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach”, vol. 5, Nat Communication, 2014. Available here. Specifically, the formulation of the individual feature calculation is covered in this supplement.
- Zwanenburg A, Leger S, Vallières M, Löck S., “Image biomarker standardisation initiative - feature definitions”, arXiv:161207003. 2016. Available here.