Radiomic Extraction and Analysis for DICOM Images
A package to extract radiomic features from DICOM CT images.
$ pip install readii
pixi
environment for a projectmkdir my_project
cd my_project
pixi init
pixi add --pypi readii
conda create -n readii python=3.9
conda activate readii
pip install readii
readii
is a tool to perform radiomic feature extraction on DICOM CT images with region of interest (ROI) segmentations as either DICOM SEG or RTSTRUCT.
$ readii [INPUT DIRECTORY] [OUTPUT DIRECTORY] \
--roi_names [str] \
--pyradiomics_setting [str] \
--negative_controls [str: shuffled_full,shuffled_roi,shuffled_non_roi,randomized_full,randomized_roi,randomized_non_roi,randomized_sampled_full,randomized_sampled_roi, randomized_sampled_non_roi] \
--parallel [flag]
--update [flag]
Negative controls are applied to one of three masks:
The three transformations are:
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Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
readii
was created by Katy Scott. It is licensed under the terms of the MIT license.
readii
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