Scripts to compute the features and develop the models from the paper "Distinguishing pure histopathological growth patterns of colorectal liver metastases on CT using deep learning and radiomics: a pilot study.", M.P.A. Starmans, F. E. Buisman et al. 2021.
Before trying out the code in this repository, we advice you to get familiar with the WORC package through the WORC tutorial: https://github.com/MStarmans91/WORCTutorial.
For the feature extraction, only the PREDICT package, at least version 3.1.13, and the subsequent dependencies are required, which can be installed through pip:
pip install "PREDICT>=3.1.13"
For the model optimization, additionally WORC, version 3.4.0, is required:
pip install "WORC==3.4.0"
The ExtractFeatures.py script can be used to extract all features. We provided you with the exact same configuration file that was used in the study. The script can be easily modified to use your own data instead of the provided example data and requires:
- An image in ITK Image format, e.g. .nii, .nii.gz, .tiff, .nrrd, .raw
- A segmentation in ITK Image format.
- Optionally, metadata in DCM format
Extracting the features from the example data should take less than 10 seconds. Using a larger image and/or mask may result in a longer computation time.
The ModelOptimization.py script can be used for the model optimization. Again, we provided you with the exact same configuration file that was used in the study. The script can be easily modified to use your own data instead of the provided example data and requires: see for more details the script itself.
Note that the script performs a dummy experiment: it supplies 10x the example features to WORC, which will result in non-separable dataset, and thus no sensible model. Usage of your own data is therefore highly recommended.
For some of the known issues, please visit the WORC FAQ: https://worc.readthedocs.io/en/latest/static/faq.html.