ctkit
CT image processing for AI and radiomics. Makes CT processing simple and reproducible.
Installation
pip install ctkit
To install pyradiomics and TotalSegmentator:
pip install 'ctkit[all]'
Quick start
import ctkit
ctkit.download("tcga-kirc", "data/tcga_kirc_raw", limit=20)
data = ctkit.Dataset("data/tcga_kirc_raw")
data.filter(min_slices=25).process("tcga-kirc", out_dir="data/processed")
# the tcga-kirc protocol segments the kidneys, so this one needs ctkit[all]
The pipeline
| Step | What it does | Why it matters |
|---|---|---|
filter |
Drop series that fail quality control, keeping a pass/fail table | Localizers, reformats and 4D series are not the acquisition you meant to analyze, and finding that out after processing wastes the expensive part |
check |
Run the same quality control without dropping anything: a result for one series, the whole table for a cohort | The measurements behind every pass and fail, which is what an exclusion criterion has to cite |
orient |
Reorient to canonical RAS | Archives disagree on storage order, so two scans of the same anatomy can arrive mirrored or transposed |
segment |
TotalSegmentator organ masks, merged with any tumor mask | Gives a region of interest when the collection ships without one |
clip |
Clamp to an intensity window (e.g. −200/300 HU) | Spends the dynamic range on the tissue you care about; caps metal artifacts |
resample |
Resample to a fixed voxel size in mm | Until scans share a voxel grid, a millimeter of anatomy is a different number of voxels in each one |
select_slice |
Keep one axial slice: the one with the most mask, or the one you name (2D mode) | How a 3D series becomes a 2D training example |
apply_mask |
Blank outside the ROI, crop to its bounding box | Removes irrelevant anatomy and makes volumes small enough to hold a cohort in memory |
crop_to_content |
Crop to the voxels above a threshold — air, once intensities are clipped | Trims the air around the body when there is no mask to crop to |
standardize_size |
Center-crop/pad to a common array shape | Fixed-size tensors, without rescaling the anatomy |
normalize |
Z-score, per volume or per dataset | Stops a model keying on per-scan intensity offsets |
save |
Write the processed series to disk | Makes the processed dataset available for training and sharing |
process |
Run the whole pipeline, with a saved configuration | Reproducibility and collaboration |
radiomics |
Extract radiomics features | For radiomic analysis. Does not require many steps above. |
Reproducibility
Every run that writes a cohort to disk writes processing_config.yaml next to
it. To reproduce a dataset, or to hand one to a collaborator:
ctkit.Dataset("data/raw").process(
"data/processed/processing_config.yaml", out_dir="rerun"
)
process takes a protocol as a ProcessingConfig, a path to a saved one, or
the name of a collection whose curated protocol to use.
Notebooks
notebooks/quickstart.ipynb walks through the
package end to end: pick a collection, download it, filter it, process it, and
extract features.
Relationship to tcia-radiology-processing
This package grew out of the protocol in
pachterlab/tcia-radiology-processing,
which documents the same pipeline as a step-by-step notebook. That repository
remains the written protocol; ctkit is the library implementation of it.
License
BSD 2-Clause. See LICENSE.
Issues and pull requests welcome.
1 maintainer
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