Metadata-Version: 2.4
Name: brain-striatum-seg
Version: 1.0.0
Summary: Automated brain and striatum segmentation from PET images using cascaded nnUNet models
Author: Bong-il Song,Yeaeun Song
Author-email: nuclesong@gmail.com, itscarolinesong@gmail.com
License: Apache 2.0
Project-URL: Bug Reports, https://github.com/itscarolinesong/brain-striatum-seg/issues
Project-URL: Models, https://zenodo.org/records/15662802
Project-URL: Documentation, https://pypi.org/project/brain-striatum-seg/
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Scientific/Engineering :: Image Processing
Requires-Python: >=3.8,<3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: nnunetv2<2.6,>=2.2
Requires-Dist: acvl-utils>=0.2
Requires-Dist: blosc2>=2.0.0
Requires-Dist: connected-components-3d
Requires-Dist: batchgenerators>=0.25
Requires-Dist: nibabel>=3.2.0
Requires-Dist: SimpleITK<=2.3.1,>=2.1.0
Requires-Dist: scikit-image>=0.19.0
Requires-Dist: numpy>=1.21.0
Requires-Dist: scipy>=1.7.0
Requires-Dist: pandas>=1.3.0
Requires-Dist: tqdm>=4.62.0
Requires-Dist: requests>=2.25.0
Requires-Dist: packaging>=20.0
Requires-Dist: psutil>=5.8.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: license
Dynamic: license-file
Dynamic: project-url
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# Brain-Striatum Segmentation

[![PyPI version](https://badge.fury.io/py/brain-striatum-seg.svg)](https://badge.fury.io/py/brain-striatum-seg)
[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)

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

```bash
pip install brain-striatum-seg
```

## 🚀 Quick Start

### Command Line Interface
```bash
# 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
```python
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.
