RSNA DICOM Anonymizer (V18 stable / V19 dev)

Install Python with tkinter (GUI library)

Windows

1. Download Python 3.12 from python.org (pytorch does not currently support 3.13)
2. Run installer
   • Select "Add python.exe to PATH"
   • Enable "tcl/tk and IDLE"

macOS

1. Install Homebrew if not present: `/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)'
2. Install Python 3.12 with Tcl/Tk:

brew install python@3.12
brew install tcl-tk

Linux (Ubuntu/Debian)

1. Install the required packages:

sudo apt update
sudo apt install software-properties-common
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt install python3.12 python3.12-tk

Verify Installation

python --version
python -m tkinter

If python + tkinter has been installed successfully a small GUI window should open

Install rsna-anonymizer package from PyPI

Stable (V18):

pip install rsna-anonymizer

V19 development pre-release (requires --pre; does not replace the stable install above):

pip install --pre rsna-anonymizer

Pin a specific dev build:

pip install --pre rsna-anonymizer==19.0.0.dev2

Verify: pip show rsna-anonymizer or rsna-anonymizer --version. See CHANGELOG for dev release notes.

TotalSegmentator, XGBoost, and related dependencies are included. Enable Harmonize, Face Blur, and Remove Pixel PHI per project in Settings → Project (or when creating a new project). Download models and apply the face license from the AI Features panel.

Execution

rsna-anonymizer

Headless Mode

You need to provide a path to a project configuration to run in headless mode rsna-anonymizer -c path/to/ProjectModel.json

Upgrading

Stable:

pip install --upgrade rsna-anonymizer

V19 dev pre-release:

pip install --upgrade --pre rsna-anonymizer

Documentation

Help files

Development

Setup

1. Setup python environment (3.12) which includes Tkinter, recommend using pyenv with MacOS & Linux
2. Ensure python is installed with Tkinter: python -m tkinter, a small GUI window should open
3. Install uv: curl -LsSf https://astral.sh/uv/install.sh | sh
4. Clone repository (for V19 work, checkout branch V19 before syncing)
5. Create virtual environment and install dependencies: uv sync --group dev
6. macOS Harmonize contrast: brew install libomp when using XGBoost/TotalSegmentator contrast
7. Enable Git pre-commit hooks (Ruff lint, same rules as CI): uv run pre-commit install

For hot-reload during UI work: uv run python scripts/dev_anonymizer.py (requires watchexec).

Hooks run on every git commit, including commits from the VS Code / Cursor Source Control UI. To skip once: git commit --no-verify.

Linting (Ruff)

Configuration: [tool.ruff] in pyproject.toml (scope: src/anonymizer/).

uv run ruff check ./src/anonymizer/
uv run ruff check ./src/anonymizer/ --fix   # safe auto-fixes only; not run in CI
uv run pre-commit run ruff-check --all-files

CI runs ruff check without --fix. Pre-commit uses the same check on staged Python files under src/anonymizer/.

Unit Testing

Test layout mirrors source: tests/controller/ → src/anonymizer/controller/, prototyping tests live under src/prototyping/*/tests/. See tests/README.md.

uv sync --group dev
uv run pytest tests/controller/tseg -q                             # may download TS weights
uv run pytest tests/controller tests/model -q   # CI suite (no view)
uv run pytest src/prototyping -q              # prototyping only
uv run pytest -q                              # full local suite (controller + view + model)

Optional: create tests/controller/.env with AWS_USERNAME and AWS_PASSWORD for S3 upload tests.

Markers (tseg_integration, dicom_integration, rsna_local_data) are documented in pyproject.toml and tests/README.md.

Translations

Languages for 17.3: en_US, de, es, fr

Ensure gettext is installed:

1. Windows: Install instructions or choco install gettext
2. Mac OSX: brew install gettext
3. Linux: sudo apt-get install gettext

Extracting messages from source files:

cd src/anonymizer/assets/locales/ ./extract_translations.sh

Updating translations:

cd src/anonymizer/assets/locales/ ./update_translations.sh

Software Architecture

Full class diagram on github here