dosemetrics 0.3.0

Measuring radiotherapy doses - plotting visualizations

DoseMetrics

A Python library for measuring radiotherapy doses and creating interactive visualizations for radiation therapy treatment planning and analysis.

📚 Documentation | 🚀 Try Live Demo | 💻 GitHub

Overview

DoseMetrics provides tools for analyzing radiation dose distributions, calculating dose-volume histograms (DVH), evaluating treatment plan quality, and creating publication-ready visualizations. This library is designed for medical physicists, radiation oncologists, and researchers working with radiotherapy treatment planning data.

Package Structure

• dosemetrics.metrics: Core dose calculation functions (DVH, scores, etc.)
• dosemetrics.io: File I/O utilities for reading dose and mask data
• dosemetrics.utils: Utility functions for compliance, plotting, etc.
• App-specific code moved to separate dosemetrics_app package (not distributed on PyPI)

Features

• Dose Analysis: Calculate and analyze 3D dose distributions
• DVH Generation: Create dose-volume histograms for organs at risk (OARs) and targets
• Quality Metrics: Compute conformity indices, homogeneity indices, and other plan quality metrics
• Compliance Checking: Evaluate dose constraints and treatment plan compliance
• Interactive Visualizations: Generate interactive plots using Plotly and Streamlit
• Comparative Analysis: Compare predicted vs. actual dose distributions
• Geometric Analysis: Compute spatial differences and overlaps between structures
• Export Capabilities: Save results in various formats (CSV, PDF, PNG)
• Command Line Interface: Basic CLI for common operations

Quick Start

Installation

Install it easily using pip:

pip install dosemetrics

For development, clone the repository and run make setup (uses uv when available to install dependencies and prepare a virtual environment):

git clone https://github.com/contouraid/dosemetrics.git
cd dosemetrics
make setup
# or manually if you prefer:
# ./scripts/setup_repo.sh

Interactive Web Application

Launch the interactive Streamlit application using the Makefile:

make app
# or
make run

Or use the shell script directly:

./scripts/run_streamlit_app.sh

Alternatively, run manually from the root directory:

PYTHONPATH=src streamlit run src/dosemetrics_app/app.py

Note: The application includes password authentication that is currently disabled for development/testing. If you need to enable authentication, uncomment the authentication check in src/dosemetrics_app/app.py and configure the secrets.toml file with user passwords.

This provides a user-friendly interface for uploading NIfTI files, analyzing dose distributions, and generating reports.

Makefile Commands

The project includes a Makefile for common operations:

make help       # Show all available commands
make setup      # Initial setup and install dependencies
make test       # Run all tests
make run        # Run the Streamlit app locally
make docs       # Serve documentation locally
make deploy     # Deploy to Hugging Face Space
make clean      # Clean up cache and temporary files
make info       # Show project information

See make help for a complete list of commands.

Documentation

Comprehensive documentation is available at contouraid.github.io/dosemetrics

• Getting Started Guide
• Using Your Own Data
• API Reference
• User Guide

Building Documentation Locally

# Install documentation dependencies
pip install -e ".[docs]"

# Serve with live-reload
make docs
# or
mkdocs serve

Usage Examples

Basic DVH Analysis

import dosemetrics

# Load dose and structure data (using the new structure)
dose_data = dosemetrics.read_from_nifti("path/to/dose.nii.gz")
structures = {"PTV": dosemetrics.read_from_nifti("path/to/ptv.nii.gz")}

# Generate DVH
dvh_df = dosemetrics.dvh_by_structure(dose_data, structures)

# Plot DVH
dosemetrics.plot_dvh(dose_data, structures, "output.pdf")

Quality Metrics Calculation

# Calculate dose summary statistics
quality_metrics = dosemetrics.dose_summary(dose_data, structures)

print(f"Quality metrics: {quality_metrics}")

Command Line Interface

# Generate DVH from command line
dosemetrics dvh dose.nii.gz structure1.nii.gz structure2.nii.gz -o dvh_results.csv

# Compute quality metrics  
dosemetrics quality dose.nii.gz structure1.nii.gz structure2.nii.gz -o quality_report.csv

# Check version
dosemetrics --version

Compliance Checking

# Define dose constraints
constraints = {
    "Brainstem": {"max_dose": 54, "unit": "Gy"},
    "Spinal_Cord": {"max_dose": 45, "unit": "Gy"},
    "Parotid_L": {"mean_dose": 26, "unit": "Gy"}
}

# Check compliance
compliance_results = dm.compliance.check_constraints(
    dose_data, structures, constraints
)

Project Structure

dosemetrics/
├── src/dosemetrics/           # Core library modules (new structure)
│   ├── io/                    # Data I/O utilities
│   │   └── data_io.py        # File loading and data reading
│   ├── metrics/              # Core dose calculations
│   │   ├── dvh.py           # DVH calculation
│   │   ├── exposure.py      # Exposure metrics (formerly metrics.py)
│   │   └── scores.py        # Scoring algorithms
│   └── utils/               # Utility functions
│       ├── comparison.py    # Plan comparison tools
│       ├── compliance.py    # Constraint checking
│       └── plot.py         # Visualization tools
├── src/dosemetrics_app/      # Streamlit web application
│   ├── app.py               # Main application entry point
│   └── tabs/                # Application tab modules
│       └── variations.py    # Variations analysis tab
├── examples/                # Usage examples and scripts
├── tests/                   # Organized unit tests
│   ├── data_io/            # Tests for I/O functionality
│   ├── metrics/            # Tests for metrics calculations
│   └── utils/              # Tests for utility functions
└── data/                   # Sample data for testing

Examples

The examples/ directory contains comprehensive examples:

• DVH Analysis: Generate and compare dose-volume histograms
• Quality Assessment: Calculate treatment plan quality indices
• Geometric Analysis: Compute structure overlaps and distances
• Interactive Plotting: Create interactive visualizations
• Report Generation: Generate automated treatment plan reports

Run any example script:

python examples/plot_dvh_interactive.py
python examples/compare_quality_index.py
python examples/generate_dvh_family.py

Supported Data Formats

• NIfTI: .nii, .nii.gz files

Development

Running Tests

Execute the test suite to ensure everything works correctly:

python -m unittest discover -s tests -p "test_*.py"

Contributing

We welcome contributions! Please see our contributing guidelines:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Requirements

• Python 3.9 or higher
• See pyproject.toml for complete dependency list

Documentation

For detailed API documentation and tutorials, visit our documentation site (coming soon).

Citation

If you use DoseMetrics in your research, please cite:

@software{dosemetrics2024,
  author = {Kamath, Amith},
  title = {DoseMetrics: A Python Library for Radiotherapy Dose Analysis},
  url = {https://github.com/contouraid/dosemetrics},
  version = {0.2.0},
  year = {2024}
}

License

This project is licensed under the Creative Commons Attribution-ShareAlike-NonCommercial 4.0 International License - see the LICENSE file for details.

Non-Commercial Use: This software is freely available for academic, research, and personal use. Commercial use requires explicit written permission from the copyright holder.

For commercial licensing inquiries, please contact the folks at contouraid.

Contributors

• Amith Kamath - Lead Developer - amithjkamath

Acknowledgments

• Medical physics community for guidance and feedback
• Open source medical imaging libraries that make this work possible
• Contributors and users who help improve the library

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions