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Automated brain and striatum segmentation from PET images using cascaded nnUNet models

Project description

Brain-Striatum Segmentation

PyPI version License: Apache 2.0 Python 3.8+

Automated brain and striatum segmentation from PET images using cascaded nnUNet models.

🧠 Overview

This package implements a two-stage segmentation pipeline:

  1. Brain Extraction: Segments the brain region from PET images
  2. Brain Cropping: Applies the brain mask to focus on brain tissue
  3. Striatum Segmentation: Segments the striatum from brain-cropped images

📦 Installation

pip install brain-striatum-seg

🚀 Quick Start

Command Line Interface

# Process single file
brain-striatum-seg -i input.nii.gz -o output_dir/

# Process multiple files
brain-striatum-seg -i input_directory/ -o output_directory/

Python API

from brain_striatum_seg import brain_striatum_segmentation

# Process and save to file
brain_striatum_segmentation("input.nii.gz", "output_dir/")

# Return nibabel image
result_img = brain_striatum_segmentation("input.nii.gz")

📚 Documentation

  • Input: PET images in NIfTI format (.nii.gz)
  • Output: Binary segmentation masks (.nii.gz)

📄 Citation

If you use this tool in your research, please cite our paper: [Your Citation Here]

Please also cite nnUNet: https://github.com/MIC-DKFZ/nnUNet

📜 License

Apache License 2.0

🤝 Contributing

Contributions welcome! Please open an issue or submit a pull request.

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