https://github.com/brainlesion/petu
pediatric brain tumor segmentation models
Science Score: 49.0%
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○CITATION.cff file
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✓.zenodo.json file
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✓DOI references
Found 2 DOI reference(s) in README -
✓Academic publication links
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○Scientific vocabulary similarity
Low similarity (17.1%) to scientific vocabulary
Keywords
Repository
pediatric brain tumor segmentation models
Basic Info
- Host: GitHub
- Owner: BrainLesion
- Language: Python
- Default Branch: main
- Homepage: https://doi.org/10.48550/arXiv.2507.22152
- Size: 150 KB
Statistics
- Stars: 3
- Watchers: 0
- Forks: 1
- Open Issues: 1
- Releases: 5
Topics
Metadata Files
README.md
PeTu
PeTu is a fully automated pipeline for segmenting pediatric brain tumors. It uses a 3D nnU-Net model trained on co-registered multi-parametric MRI scans, including T1c, T1n, T2w, and T2f sequences. Subsequently, the model provides segmented tumor regions, including:
- T2-hyperintense region (T2H) – typically encompassing solid tumor mass and associated edema.
- Enhancing tumor (ET) – regions with contrast uptake, indicative of active or aggressive tumor areas.
- Cystic component (CC) – fluid-filled regions often seen in certain pediatric tumor types.
Features
Installation
With a Python 3.10+ environment, you can install petu directly from PyPI:
bash
pip install petu
Use Cases and Tutorials
A minimal example to create a segmentation could look like this:
```python from petu import Inferer
inferer = Inferer()
Save NIfTI files
inferer.infer( t1c="path/to/t1c.nii.gz", fla="path/to/fla.nii.gz", t1="path/to/t1.nii.gz", t2="path/to/t2.nii.gz", ETsegmentationfile="example/ET.nii.gz", CCsegmentationfile="example/CC.nii.gz", T2Hsegmentationfile="example/T2H.nii.gz", )
Or directly use pre-loaded NumPy data. (Both outputs work as well)
et, cc, t2h = inferer.infer( t1c=t1cnp, fla=flanp, t1=t1np, t2=t2np, ) ```
[!NOTE]
If you're interested in the PeTu package, the Pediatric Segmentation may also be of interest. <!-- For more examples and details please refer to our extensive Notebook tutorials here NBViewer (GitHub). For the best experience open the notebook in Colab. -->
Citation
Please support our development by citing the following manuscripts:
@misc{piffer2025enhancingefficiencypaediatricbrain,
title={Enhancing efficiency in paediatric brain tumour segmentation using a pathologically diverse single-center clinical dataset},
author={A. Piffer and J. A. Buchner and A. G. Gennari and P. Grehten and S. Sirin and E. Ross and I. Ezhov and M. Rosier and J. C. Peeken and M. Piraud and B. Menze and A. Guerreiro Stücklin and A. Jakab and F. Kofler},
year={2025},
eprint={2507.22152},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2507.22152},
}
Contributing
We welcome all kinds of contributions from the community!
Reporting Bugs, Feature Requests and Questions
Please open a new issue here.
Code contributions
Nice to have you on board! Please have a look at our CONTRIBUTING.md file.
Owner
- Name: BrainLesion
- Login: BrainLesion
- Kind: organization
- Repositories: 1
- Profile: https://github.com/BrainLesion
GitHub Events
Total
- Create event: 11
- Release event: 4
- Issues event: 10
- Watch event: 1
- Delete event: 7
- Member event: 1
- Issue comment event: 10
- Push event: 29
- Pull request review comment event: 4
- Pull request event: 23
- Pull request review event: 13
Last Year
- Create event: 11
- Release event: 4
- Issues event: 10
- Watch event: 1
- Delete event: 7
- Member event: 1
- Issue comment event: 10
- Push event: 29
- Pull request review comment event: 4
- Pull request event: 23
- Pull request review event: 13
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 7
- Total pull requests: 13
- Average time to close issues: 11 days
- Average time to close pull requests: about 17 hours
- Total issue authors: 2
- Total pull request authors: 3
- Average comments per issue: 0.71
- Average comments per pull request: 0.15
- Merged pull requests: 10
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 7
- Pull requests: 13
- Average time to close issues: 11 days
- Average time to close pull requests: about 17 hours
- Issue authors: 2
- Pull request authors: 3
- Average comments per issue: 0.71
- Average comments per pull request: 0.15
- Merged pull requests: 10
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- MarcelRosier (5)
- neuronflow (2)
Pull Request Authors
- MarcelRosier (7)
- evamariie (3)
- neuronflow (3)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 19 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 6
- Total maintainers: 2
pypi.org: petu
- Homepage: https://www.TODO.com
- Documentation: https://www.TODO.com
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Latest release: 0.0.6
published 7 months ago
Rankings
Maintainers (2)
Dependencies
- actions/checkout v3 composite
- actions/setup-python v4 composite
- actions/checkout v4 composite
- actions/setup-python v5 composite
- flake8 >=4.0.1 develop
- pytest >=8.0.0 develop
- pytest-cov >=5.0.0 develop
- Sphinx >=7.0.0 docs
- furo >=2024.8.6 docs
- myst-parser >=2.0.0 docs
- sphinx-copybutton >=0.5.2 docs
- python >=3.9
- 118 dependencies