Radiomic Extraction and Analysis for DICOM Images

A package to extract radiomic features from DICOM CT images.

$ pip install readii

mkdir 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:

1. full = voxels in the entire image
2. roi = just voxels within the specified region of interest (ROI) in the segmentation
3. non_roi = all voxels except the ROI.

The three transformations are:

1. shuffle = shuffle all voxels in the specified mask
2. randomized = randomly generate new values within the original range within the specified mask
3. randomized_sampled = randomly sample original values with replacement to get new values within the specified mask

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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 was created with cookiecutter and the py-pkgs-cookiecutter template.