WORC v3.6.3
===========
Workflow for Optimal Radiomics Classification
---------------------------------------------
Information
-----------
+-------------------+------------------+------------------+------------+
| Unit test | Documentation | PyPi | Citing |
| | | | WORC |
+===================+==================+==================+============+
| |image1| | |image2| | |image3| | |image4| |
+-------------------+------------------+------------------+------------+
Introduction
============
WORC is an open-source python package for the easy execution and fully
automatic construction and optimization of radiomics workflows.
We aim to establish a general radiomics platform supporting easy
integration of other tools. With our modular build and support of
different software languages (python, MATLAB, ruby, java etc.), we want
to facilitate and stimulate collaboration, standardisation and
comparison of different radiomics approaches. By combining this in a
single framework, we hope to find a universal radiomics strategy that
can address various problems.
License
-------
This package is covered by the open source `APACHE 2.0
License <APACHE-LICENSE-2.0>`__.
When using WORC, please cite this repository and the paper describing
WORC as as follows:
.. code:: bibtex
@article{starmans2021reproducible,
title = {Reproducible radiomics through automated machine learning validated on twelve clinical applications},
author = {Martijn P. A. Starmans and Sebastian R. van der Voort and Thomas Phil and Milea J. M. Timbergen and Melissa Vos and Guillaume A. Padmos and Wouter Kessels and David Hanff and Dirk J. Grunhagen and Cornelis Verhoef and Stefan Sleijfer and Martin J. van den Bent and Marion Smits and Roy S. Dwarkasing and Christopher J. Els and Federico Fiduzi and Geert J. L. H. van Leenders and Anela Blazevic and Johannes Hofland and Tessa Brabander and Renza A. H. van Gils and Gaston J. H. Franssen and Richard A. Feelders and Wouter W. de Herder and Florian E. Buisman and Francois E. J. A. Willemssen and Bas Groot Koerkamp and Lindsay Angus and Astrid A. M. van der Veldt and Ana Rajicic and Arlette E. Odink and Mitchell Deen and Jose M. Castillo T. and Jifke Veenland and Ivo Schoots and Michel Renckens and Michail Doukas and Rob A. de Man and Jan N. M. IJzermans and Razvan L. Miclea and Peter B. Vermeulen and Esther E. Bron and Maarten G. Thomeer and Jacob J. Visser and Wiro J. Niessen and Stefan Klein},
year = {2021},
eprint = {2108.08618},
archivePrefix = {arXiv},
primaryClass = {eess.IV}
}
@software{starmans2018worc,
author = {Martijn P. A. Starmans and Thomas Phil and Sebastian R. van der Voort and Stefan Klein},
title = {Workflow for Optimal Radiomics Classification (WORC)},
year = {2018},
publisher = {Zenodo},
doi = {10.5281/zenodo.3840534},
url = {https://github.com/MStarmans91/WORC}
}
For the DOI, visit |image5|.
Disclaimer
----------
This package is still under development. We try to thoroughly test and
evaluate every new build and function, but bugs can off course still
occur. Please contact us through the channels below if you find any and
we will try to fix them as soon as possible, or create an issue on this
Github.
Tutorial, documentation and dataset
-----------------------------------
The WORC tutorial is hosted at
https://github.com/MStarmans91/WORCTutorial.
The official documentation can be found at https://worc.readthedocs.io.
The publicly released WORC database is described in the following paper:
.. code:: bibtex
@article {Starmans2021WORCDatabase,
author = {Starmans, Martijn P.A. and Timbergen, Milea J.M. and Vos, Melissa and Padmos, Guillaume A. and Gr{\"u}nhagen, Dirk J. and Verhoef, Cornelis and Sleijfer, Stefan and van Leenders, Geert J.L.H. and Buisman, Florian E. and Willemssen, Francois E.J.A. and Koerkamp, Bas Groot and Angus, Lindsay and van der Veldt, Astrid A.M. and Rajicic, Ana and Odink, Arlette E. and Renckens, Michel and Doukas, Michail and de Man, Rob A. and IJzermans, Jan N.M. and Miclea, Razvan L. and Vermeulen, Peter B. and Thomeer, Maarten G. and Visser, Jacob J. and Niessen, Wiro J. and Klein, Stefan},
title = {The WORC database: MRI and CT scans, segmentations, and clinical labels for 930 patients from six radiomics studies},
elocation-id = {2021.08.19.21262238},
year = {2021},
doi = {10.1101/2021.08.19.21262238},
URL = {https://www.medrxiv.org/content/early/2021/08/25/2021.08.19.21262238},
eprint = {https://www.medrxiv.org/content/early/2021/08/25/2021.08.19.21262238.full.pdf},
journal = {medRxiv}
}
The code to download the WORC database and reproduce our experiments can
be found at https://github.com/MStarmans91/WORCDatabase.
Installation
------------
WORC supports Unix and Windows systems with Python 3.6+: the `unit
tests <https://github.com/MStarmans91/WORC/actions?query=workflow%3A%22Unit+test%22>`__
are performed on the latest Ubuntu and Windows versions with Python 3.7.
For detailed installation instructions, please check `the ReadTheDocs
installation
guidelines <https://worc.readthedocs.io/en/latest/static/quick_start.html#installation>`__.
The package can be installed through pip:
::
pip install WORC
Alternatively, you can directly install WORC from this repository:
::
python setup.py install
Make sure you install the requirements first:
::
pip install -r requirements.txt
3rd-party packages used in WORC:
--------------------------------
- SimpleITK (Image loading and preprocessing)
- `Pyradiomics <https://github.com/radiomics/pyradiomics>`__
- `PREDICT <https://github.com/Svdvoort/PREDICTFastr>`__
- scikit-learn
- imbalanced-learn
- xgboost
- `fastr (Workflow design and
building) <http://fastr.readthedocs.io>`__
- `ComBat <https://github.com/Jfortin1/ComBatHarmonization>`__
(optional)
See for other python packages the `requirements
file <requirements.txt>`__.
Start
-----
We suggest you start with the `WORC
Tutorial <https://github.com/MStarmans91/WORCTutorial>`__. Besides a
Jupyter notebook with instructions, we provide there also an example
script for you to get started with.
Contact
-------
We are happy to help you with any questions. Please sent us a mail or
place an issue on the Github.
We welcome contributions to WORC. For the moment, converting your
toolbox into a FASTR tool is satisfactory: see also `the fastr tool
development
documentation <https://fastr.readthedocs.io/en/stable/static/user_manual.html#create-your-own-tool>`__.
Optional
--------
Besides the default installation, there are several optional packages
you could install to support WORC.
Graphviz
~~~~~~~~
WORC can draw the network and save it as a SVG image using
`graphviz <https://www.graphviz.org/>`__. In order to do so, please make
sure you install graphviz. On Ubuntu, simply run
::
apt install graphiv
On Windows, follow the installation instructions provided on the
graphviz website. Make sure you add the executable to the PATH when
prompted.
Elastix
~~~~~~~
Image registration is included in WORC through `elastix and
transformix <http://elastix.isi.uu.nl/>`__. In order to use elastix,
please download the binaries and place them in your
``fastr.config.mounts['apps']`` path. Check the elastix tool description
for the correct subdirectory structure. For example, on Linux, the
binaries and libraries should be in ``"../apps/elastix/4.8/install/"``
and ``"../apps/elastix/4.8/install/lib"`` respectively.
Note: optionally, you can tell WORC to copy the metadata from the image
file to the segmentation file before applying the deformation field.
This requires ITK and ITKTools: see `the ITKTools
github <https://github.com/ITKTools/ITKTools>`__ for installation
instructions.
XNAT
~~~~
We use the XNATpy package to connect the toolbox to the XNAT online
database platforms. You will only need this when you use the example
dataset we provided, or if you want to download or upload data from or
to XNAT. We advise you to specify your account settings in a .netrc file
when using this feature for your own datasets, such that you do not need
to input them on every request.
.. |image1| image:: https://github.com/MStarmans91/WORC/workflows/Unit%20test/badge.svg
:target: https://github.com/MStarmans91/WORC/actions?query=workflow%3A%22Unit+test%22
.. |image2| image:: https://readthedocs.org/projects/worc/badge/?version=latest
:target: https://worc.readthedocs.io/en/latest/?badge=latest
.. |image3| image:: https://badge.fury.io/py/WORC.svg
:target: https://badge.fury.io/py/WORC
.. |image4| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3840534.svg
:target: https://zenodo.org/badge/latestdoi/92295542
.. |image5| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3840534.svg
:target: https://zenodo.org/badge/latestdoi/92295542
Raw data
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"description": "WORC v3.6.3\n===========\n\nWorkflow for Optimal Radiomics Classification\n---------------------------------------------\n\nInformation\n-----------\n\n+-------------------+------------------+------------------+------------+\n| Unit test | Documentation | PyPi | Citing |\n| | | | WORC |\n+===================+==================+==================+============+\n| |image1| | |image2| | |image3| | |image4| |\n+-------------------+------------------+------------------+------------+\n\nIntroduction\n============\n\nWORC is an open-source python package for the easy execution and fully\nautomatic construction and optimization of radiomics workflows.\n\nWe aim to establish a general radiomics platform supporting easy\nintegration of other tools. With our modular build and support of\ndifferent software languages (python, MATLAB, ruby, java etc.), we want\nto facilitate and stimulate collaboration, standardisation and\ncomparison of different radiomics approaches. By combining this in a\nsingle framework, we hope to find a universal radiomics strategy that\ncan address various problems.\n\nLicense\n-------\n\nThis package is covered by the open source `APACHE 2.0\nLicense <APACHE-LICENSE-2.0>`__.\n\nWhen using WORC, please cite this repository and the paper describing\nWORC as as follows:\n\n.. code:: bibtex\n\n @article{starmans2021reproducible,\n title = {Reproducible radiomics through automated machine learning validated on twelve clinical applications}, \n author = {Martijn P. A. Starmans and Sebastian R. van der Voort and Thomas Phil and Milea J. M. Timbergen and Melissa Vos and Guillaume A. Padmos and Wouter Kessels and David Hanff and Dirk J. Grunhagen and Cornelis Verhoef and Stefan Sleijfer and Martin J. van den Bent and Marion Smits and Roy S. Dwarkasing and Christopher J. Els and Federico Fiduzi and Geert J. L. H. van Leenders and Anela Blazevic and Johannes Hofland and Tessa Brabander and Renza A. H. van Gils and Gaston J. H. Franssen and Richard A. Feelders and Wouter W. de Herder and Florian E. Buisman and Francois E. J. A. Willemssen and Bas Groot Koerkamp and Lindsay Angus and Astrid A. M. van der Veldt and Ana Rajicic and Arlette E. Odink and Mitchell Deen and Jose M. Castillo T. and Jifke Veenland and Ivo Schoots and Michel Renckens and Michail Doukas and Rob A. de Man and Jan N. M. IJzermans and Razvan L. Miclea and Peter B. Vermeulen and Esther E. Bron and Maarten G. Thomeer and Jacob J. Visser and Wiro J. Niessen and Stefan Klein},\n year = {2021},\n eprint = {2108.08618},\n archivePrefix = {arXiv},\n primaryClass = {eess.IV}\n }\n\n @software{starmans2018worc,\n author = {Martijn P. A. Starmans and Thomas Phil and Sebastian R. van der Voort and Stefan Klein},\n title = {Workflow for Optimal Radiomics Classification (WORC)},\n year = {2018},\n publisher = {Zenodo},\n doi = {10.5281/zenodo.3840534},\n url = {https://github.com/MStarmans91/WORC}\n }\n\nFor the DOI, visit |image5|.\n\nDisclaimer\n----------\n\nThis package is still under development. We try to thoroughly test and\nevaluate every new build and function, but bugs can off course still\noccur. Please contact us through the channels below if you find any and\nwe will try to fix them as soon as possible, or create an issue on this\nGithub.\n\nTutorial, documentation and dataset\n-----------------------------------\n\nThe WORC tutorial is hosted at\nhttps://github.com/MStarmans91/WORCTutorial.\n\nThe official documentation can be found at https://worc.readthedocs.io.\n\nThe publicly released WORC database is described in the following paper:\n\n.. code:: bibtex\n\n @article {Starmans2021WORCDatabase,\n author = {Starmans, Martijn P.A. and Timbergen, Milea J.M. and Vos, Melissa and Padmos, Guillaume A. and Gr{\\\"u}nhagen, Dirk J. and Verhoef, Cornelis and Sleijfer, Stefan and van Leenders, Geert J.L.H. and Buisman, Florian E. and Willemssen, Francois E.J.A. and Koerkamp, Bas Groot and Angus, Lindsay and van der Veldt, Astrid A.M. and Rajicic, Ana and Odink, Arlette E. and Renckens, Michel and Doukas, Michail and de Man, Rob A. and IJzermans, Jan N.M. and Miclea, Razvan L. and Vermeulen, Peter B. and Thomeer, Maarten G. and Visser, Jacob J. and Niessen, Wiro J. and Klein, Stefan},\n title = {The WORC database: MRI and CT scans, segmentations, and clinical labels for 930 patients from six radiomics studies},\n elocation-id = {2021.08.19.21262238},\n year = {2021},\n doi = {10.1101/2021.08.19.21262238},\n URL = {https://www.medrxiv.org/content/early/2021/08/25/2021.08.19.21262238},\n eprint = {https://www.medrxiv.org/content/early/2021/08/25/2021.08.19.21262238.full.pdf},\n journal = {medRxiv}\n }\n\nThe code to download the WORC database and reproduce our experiments can\nbe found at https://github.com/MStarmans91/WORCDatabase.\n\nInstallation\n------------\n\nWORC supports Unix and Windows systems with Python 3.6+: the `unit\ntests <https://github.com/MStarmans91/WORC/actions?query=workflow%3A%22Unit+test%22>`__\nare performed on the latest Ubuntu and Windows versions with Python 3.7.\nFor detailed installation instructions, please check `the ReadTheDocs\ninstallation\nguidelines <https://worc.readthedocs.io/en/latest/static/quick_start.html#installation>`__.\n\nThe package can be installed through pip:\n\n::\n\n pip install WORC\n\nAlternatively, you can directly install WORC from this repository:\n\n::\n\n python setup.py install\n\nMake sure you install the requirements first:\n\n::\n\n pip install -r requirements.txt\n\n3rd-party packages used in WORC:\n--------------------------------\n\n- SimpleITK (Image loading and preprocessing)\n- `Pyradiomics <https://github.com/radiomics/pyradiomics>`__\n- `PREDICT <https://github.com/Svdvoort/PREDICTFastr>`__\n- scikit-learn\n- imbalanced-learn\n- xgboost\n- `fastr (Workflow design and\n building) <http://fastr.readthedocs.io>`__\n- `ComBat <https://github.com/Jfortin1/ComBatHarmonization>`__\n (optional)\n\nSee for other python packages the `requirements\nfile <requirements.txt>`__.\n\nStart\n-----\n\nWe suggest you start with the `WORC\nTutorial <https://github.com/MStarmans91/WORCTutorial>`__. Besides a\nJupyter notebook with instructions, we provide there also an example\nscript for you to get started with.\n\nContact\n-------\n\nWe are happy to help you with any questions. Please sent us a mail or\nplace an issue on the Github.\n\nWe welcome contributions to WORC. For the moment, converting your\ntoolbox into a FASTR tool is satisfactory: see also `the fastr tool\ndevelopment\ndocumentation <https://fastr.readthedocs.io/en/stable/static/user_manual.html#create-your-own-tool>`__.\n\nOptional\n--------\n\nBesides the default installation, there are several optional packages\nyou could install to support WORC.\n\nGraphviz\n~~~~~~~~\n\nWORC can draw the network and save it as a SVG image using\n`graphviz <https://www.graphviz.org/>`__. In order to do so, please make\nsure you install graphviz. On Ubuntu, simply run\n\n::\n\n apt install graphiv\n\nOn Windows, follow the installation instructions provided on the\ngraphviz website. Make sure you add the executable to the PATH when\nprompted.\n\nElastix\n~~~~~~~\n\nImage registration is included in WORC through `elastix and\ntransformix <http://elastix.isi.uu.nl/>`__. In order to use elastix,\nplease download the binaries and place them in your\n``fastr.config.mounts['apps']`` path. Check the elastix tool description\nfor the correct subdirectory structure. For example, on Linux, the\nbinaries and libraries should be in ``\"../apps/elastix/4.8/install/\"``\nand ``\"../apps/elastix/4.8/install/lib\"`` respectively.\n\nNote: optionally, you can tell WORC to copy the metadata from the image\nfile to the segmentation file before applying the deformation field.\nThis requires ITK and ITKTools: see `the ITKTools\ngithub <https://github.com/ITKTools/ITKTools>`__ for installation\ninstructions.\n\nXNAT\n~~~~\n\nWe use the XNATpy package to connect the toolbox to the XNAT online\ndatabase platforms. 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