Workflow for Optimal Radiomics Classification.
Project description
WORC v2.1.3
Workflow for Optimal Radiomics Classification
WORC is an open-source python package for the easy execution of full Radiomics pipelines.
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.
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
The WORC tutorial is hosted in a separate repository.
Documentation
For more information, see our the Wiki on this Github.
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.
Installation
WORC currently only supports Unix with Python 2 (>2.7.6) systems. Windows is not supported, although WORC can still work under windows.
Please first install PREDICT:
pip install PREDICT
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
Several tools have some (mandatory) prerequisites which are listed below. We highly recommend you to install these to maximally profit from our toolbox.
PREDICT
Most of the default tools in WORC use PREDICT, our in-house feature extraction and classification toolbox. Currently, you do need to manually install PREDICT from the Github or with pip:
pip install PREDICT
Fastr Configuration
The installation will create a FASTR configuration file in the $HOME/.fastr/config.d folder. This file is used for configuring fastr, the pipeline execution toolbox we use. More information can be found at the FASTR website. In this file, so called mounts are defined, which are used to locate the WORC tools and your inputs and outputs. Please inspect the mounts and change them if neccesary.
Only if you are using FASTR < 1.3.0, you need to manually add the WORC tools, datatypes and mounts to your FASTR configuration ($HOME/.fastr/config.py). This concerns the following additions:
Optional: Graphviz
WORC can draw the network and save it as a SVG image using graphviz. In order to do so, please make sure you install graphviz:
apt install graphiv
Optional: Elastix
Image registration is included in WORC through elastix and transformix. 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 Install_ITK file for installation instructions. More info on using the copying of metadata can be found on our Github Wiki.
Optional: XNAT
We use the XNATpy package to connect the toolbox to the XNAT online database platforms. You will only need this when 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, such that you do not need to input them on every request:
echo "machine images.xnat.org > login admin > password admin" > ~/.netrc chmod 600 ~/.netrc
3rd-party packages used in WORC:
FASTR (Workflow design and building)
xnat (Collecting data from XNAT)
SimpleITK (Image loading and preprocessing)
Our in-house package PREDICT
See for other requirements the requirements file.
Start
We suggest you start with the WORC Tutorial. Besides a Jupter notebook with instructions, we provide there also an example script for you to get started with. Make sure you input your own data as the sources. Also, check out the unit tests of several tools in the WORC/resources/fastr_tests directory. The example is explained in more detail in the Wiki on this Github.
WIP
We are working on improving the documentation.
We are working on organizing clinically relevant datasets for examples and unit tests.
We will merge to Python 3 support in the coming months (April 2019), as soon as FASTR moves to Python 3.
License
This package is covered by the open source APACHE 2.0 License.
When using WORC, please cite this repository.
Contact
We are happy to help you with any questions. Please contact us on the WORC google group.
We welcome contributions to WORC. We will soon make some guidelines. For the moment, converting your toolbox into a FASTR tool will be satisfactory.
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