dicom-anvil 0.1.1

Enterprise-grade medical-imaging pipeline: ingest, de-identify, convert, and QC DICOM for 3D Slicer and ITK-SNAP.

dicom-forge

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

dicom-forge takes a folder of DICOM, de-identifies it, runs quality control, and converts it into the formats clinical-research viewers load natively — NIfTI (.nii.gz), NRRD (.nrrd), and Slicer's segmentation format (.seg.nrrd). Every run produces a serialisable audit record.

It is the headless core of a two-repo system; its companion, slicer-forge, wraps it in a 3D Slicer extension GUI.

📌 New here? Read the project showcase for the big picture — the problem, the architecture, who it's for, and the engineering highlights.

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Why this exists

Real imaging pipelines separate a testable, headless core from a thin GUI. 3D Slicer itself is built this way: ITK/VTK do the work, the GUI is a shell on top. dicom-forge is that core — so the logic is unit-tested in CI without needing Slicer installed, and the same code runs from the command line, from Python, or inside Slicer's Python console.

Features

• Ingestion — recursive DICOM discovery, series grouping, geometry-aware slice ordering (handles oblique acquisitions), rescale-slope/intercept applied (CT in HU).
• De-identification — three escalating levels modelled on the DICOM PS3.15 Basic Profile, with deterministic salted-hash pseudonymisation of PatientID.
• Quality control — slice count, geometry consistency, slice-spacing regularity, and intensity statistics, split into blocking errors vs. non-blocking warnings.
• Conversion — geometry-preserving export to NIfTI / NRRD via SimpleITK (the ITK core shared with Slicer & ITK-SNAP).
• Segmentation — write Slicer-native .seg.nrrd label maps with named, coloured segments.
• Typed & validated — Pydantic models everywhere, py.typed, strict mypy.
• Two interfaces — a Rich CLI (dicomforge) and a clean Python API.
• Native ITK pre-flight — a companion C++/ITK CLI, dicom-probe, reads a series through ITK's own GDCM path (the engine under Slicer/ITK-SNAP) and reports the true volume geometry as JSON.

Installation

pip install dicom-anvil            # core (ingest, de-id, QC)
pip install "dicom-anvil[convert]" # + SimpleITK/pynrrd/nibabel for conversion

Names: the package installs as dicom-anvil (the PyPI name dicom-forge was already taken) and imports as dicomforge; the repository stays dicom-forge.

The conversion stack (SimpleITK et al.) is an optional extra so the core stays lightweight and CI-friendly. Calling a conversion function without it raises a clear MissingDependencyError telling you exactly what to install.

Quick start

Command line

# List the series in a folder
dicomforge inspect ./study

# Run QC and print a report
dicomforge qc ./study

# Full pipeline: de-identify -> QC -> convert to Slicer NRRD
dicomforge convert ./study ./out/patient01 --format nrrd --deid-level moderate

Python

from dicomforge import run_pipeline, PipelineConfig, OutputFormat

result = run_pipeline(
    "./study",
    "./out/patient01",
    config=PipelineConfig(output_format=OutputFormat.NRRD),
)
print(result.qc.passed)                 # True / False
print(result.conversion.output_path)    # ./out/patient01.nrrd
print(result.model_dump_json(indent=2)) # full audit record

Pipeline order (and why it matters)

ingest ──> de-identify ──> QC ──> convert

De-identification runs before conversion, so any file written to disk is already free of direct identifiers. De-id is performed in memory — your source DICOM is never modified.

⚠️ De-identification is risk reduction, not a legal guarantee. Burned-in pixel annotations and private vendor tags can still carry PHI. Always review output before releasing it outside a controlled environment.

Development

python -m venv .venv && . .venv/Scripts/activate   # Windows
pip install -e ".[dev,convert]"
pytest                      # run the suite (synthetic DICOM, no real data needed)
ruff check . && mypy        # lint + type-check

License

PolyForm Noncommercial License 1.0.0 © Angela Hudson

Noncommercial use — personal, research, educational, and other noncommercial purposes as defined by the license — is free. Any commercial use requires a separate license from the copyright holder. See LICENSE for the full terms, or open an issue to ask about commercial licensing.