https://github.com/aim-harvard/pyradiomics
Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c/community/radiomics
https://github.com/aim-harvard/pyradiomics
cancer-imaging-research computational-imaging docker feature-extraction ibsi medical-imaging nci-itcr nci-qin python radiomics radiomics-feature-extraction radiomics-features tcia-dac
Last synced: 23 days ago
JSON representation
Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c/community/radiomics
- Host: GitHub
- URL: https://github.com/aim-harvard/pyradiomics
- Owner: AIM-Harvard
- License: bsd-3-clause
- Created: 2015-09-15T15:11:28.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2024-10-21T16:31:16.000Z (8 months ago)
- Last Synced: 2024-10-29T15:38:09.915Z (7 months ago)
- Topics: cancer-imaging-research, computational-imaging, docker, feature-extraction, ibsi, medical-imaging, nci-itcr, nci-qin, python, radiomics, radiomics-feature-extraction, radiomics-features, tcia-dac
- Language: Jupyter Notebook
- Homepage: http://pyradiomics.readthedocs.io/
- Size: 53.3 MB
- Stars: 1,156
- Watchers: 47
- Forks: 499
- Open Issues: 146
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.rst
- Contributing: CONTRIBUTING.rst
- License: LICENSE.txt
Awesome Lists containing this project
README
# pyradiomics v3.1.0
<-- ## Build Status
| Linux / MacOS | Windows |
| ----------------------------- | ----------------------------- |
| | |
-->
## Radiomics feature extraction in Python
This is an open-source python package for the extraction of Radiomics features
from medical imaging.
With this package we aim to establish a reference standard for Radiomic
Analysis, and provide a tested and maintained 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.
The platform supports both the feature extraction in 2D and 3D and can be used
to calculate single values per feature for a region of interest
("segment-based") or to generate feature maps ("voxel-based").
**Not intended for clinical use.**
**If you publish any work which uses this package, please cite the following
publication:** _van Griethuysen, J. J. M., Fedorov, A., Parmar, C., Hosny, A.,
Aucoin, N., Narayan, V., Beets-Tan, R. G. H., Fillion-Robin, J. C., Pieper, S.,
Aerts, H. J. W. L. (2017). Computational Radiomics System to Decode the
Radiographic Phenotype. Cancer Research, 77(21), e104–e107.
https://doi.org/10.1158/0008-5472.CAN-17-0339_
### Join the Community!
Please join the
[Radiomics community section of the 3D Slicer Discourse](https://discourse.slicer.org/c/community/radiomics/23).
### Feature Classes
Currently supports the following feature classes:
- First Order Statistics
- Shape-based (2D and 3D)
- Gray Level Co-occurrence Matrix (GLCM)
- Gray Level Run Length Matrix (GLRLM)
- Gray Level Size Zone Matrix (GLSZM)
- Gray Level Dependece Matrix (GLDM)
- Neighboring Gray Tone Difference Matrix (NGTDM)
### 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
- Gradient (Magnitude)
- Local Binary Pattern (LBP) 2D / 3D
### 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](http://pyradiomics.readthedocs.io/).
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](http://radiomics.io/pyradiomics.html).
### Installation
PyRadiomics is OS independent and compatible with Python >= 3.5. Pre-built
binaries are available on PyPi and Conda. To install PyRadiomics, ensure you
have python installed and run:
`python -m pip install pyradiomics`
Detailed installation instructions, as well as instructions for building
PyRadiomics from source, are available in the
[documentation](http://pyradiomics.readthedocs.io/en/latest/installation.html).
### Docker
PyRadiomics also supports [Dockers](https://www.docker.com/). Currently, 2
dockers are available:
The first one is a [Jupyter notebook](http://jupyter.org/) with PyRadiomics
pre-installed with example Notebooks.
To get the Docker:
docker pull radiomics/pyradiomics:latest
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 second is a docker which exposes the PyRadiomics CLI interface. To get the
CLI-Docker:
docker pull radiomics/pyradiomics:CLI
You can then use the PyRadiomics CLI as follows:
docker run radiomics/pyradiomics:CLI --help
For more information on using docker, see
[here](https://pyradiomics.readthedocs.io/en/latest/installation.html#use-pyradiomics-docker)
### Usage
PyRadiomics can be easily used in a Python script through the `featureextractor`
module. Furthermore, PyRadiomics provides a commandline script, `pyradiomics`,
for both single image extraction and batchprocessing. Finally, a convenient
front-end interface is provided as the 'Radiomics' extension for 3D Slicer,
available [here](https://github.com/AIM-Harvard/SlicerRadiomics).
### 3rd-party packages used in pyradiomics:
- SimpleITK (Image loading and preprocessing)
- numpy (Feature calculation)
- PyWavelets (Wavelet filter)
- pykwalify (Enabling yaml parameters file checking)
- scipy (Only for LBP filter, install separately to enable this filter)
- scikit-image (Only for LBP filter, install separately to enable this filter)
- trimesh (Only for LBP filter, install separately to enable this filter)
See also the requirements section of the [pyproject file](pyproject.toml).
### 3D Slicer
PyRadiomics is also available as an
[extension](https://github.com/AIM-Harvard/SlicerRadiomics) to
[3D Slicer](slicer.org). Download and install the 3D slicer
[nightly build](http://download.slicer.org/), the extension is then available in
the extension manager under "SlicerRadiomics".
### License
This package is covered by the open source [3-clause BSD License](LICENSE.txt).
### Developers
- [Joost van Griethuysen](https://github.com/JoostJM)1,3,4
- [Andriy Fedorov](https://github.com/fedorov)2
- [Nicole Aucoin](https://github.com/naucoin)2
- [Jean-Christophe Fillion-Robin](https://github.com/jcfr)5
- [Ahmed Hosny](https://github.com/ahmedhosny)1
- [Steve Pieper](https://github.com/pieper)6
- [Hugo Aerts (PI)](https://github.com/hugoaerts)1,2
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
[Radiomics community section of the 3D Slicer Discourse](https://discourse.slicer.org/c/community/radiomics/23).
We welcome contributions to PyRadiomics. Please read the
[contributing guidelines](CONTRIBUTING.rst) on how to contribute to PyRadiomics.
**This work was supported in part by the US National Cancer Institute grants:
U24CA194354 - QUANTITATIVE RADIOMICS SYSTEM DECODING THE TUMOR PHENOTYPE and
U01CA190234 - TUMOR GENOTYPE AND RADIOMIC PHENOTYPE IN LUNG CANCER**