dicom-forge

An enterprise-grade medical-imaging pipeline that prepares DICOM for 3D Slicer and ITK-SNAP.

It ingests DICOM, de-identifies PHI, runs quality control, and converts to the formats research viewers load natively — NIfTI (.nii.gz), NRRD (.nrrd), and Slicer's segmentation format (.seg.nrrd).

Install

pip install "dicom-anvil[convert]"

Installed as dicom-anvil (the name dicom-forge was already taken on PyPI); imported as dicomforge. The source repository is dicom-forge.

60-second tour

dicomforge inspect ./study                 # what's in this folder?
dicomforge qc ./study                       # is it analysis-ready?
dicomforge convert ./study ./out/case01 -f nrrd   # de-id -> QC -> convert

from dicomforge import run_pipeline, PipelineConfig, OutputFormat

result = run_pipeline("./study", "./out/case01",
                      config=PipelineConfig(output_format=OutputFormat.NRRD))
print(result.model_dump_json(indent=2))

See Concepts for the pipeline design, the CLI reference for every command, and the API reference for the Python surface.

Patient data

De-identification is best-effort risk reduction, not a compliance guarantee. Never commit real patient data. See the project SECURITY.md.