PeTu documentation¶

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¶

Data Requirements¶

PeTu is trained on pediatric brain MRI data from the Children’s Hospital Zurich (Kispi), including cases with optic glioma affecting the optic nerve.

The model expects multi-parametric MRI scans (T1c, T1n, T2w, T2f) that are co-registered to T1c and then brought into SRI-24 brain atlas space.

Important

However, it may be worth experimenting with skull-stripped (BET) or defaced brain scans depending on your specific use case.

We recommend using the [preprocessing package](https://github.com/BrainLesion/preprocessing), part of the [BrainLesion Suite](https://github.com/BrainLesion), to design custom preprocessing pipelines tailored to your specific needs.

## Installation

With a Python 3.10+ environment, you can install `petu` directly from [PyPI](https://pypi.org/project/petu/):

```bash
pip install petu

Use Cases and Tutorials¶

A minimal example to create a segmentation could look like this:

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",
    ET_segmentation_file="example/ET.nii.gz",
    CC_segmentation_file="example/CC.nii.gz",
    T2H_segmentation_file="example/T2H.nii.gz",
)

# Or directly use pre-loaded NumPy data. (Both outputs work as well)
et, cc, t2h = inferer.infer(
    t1c=t1c_np,
    fla=fla_np,
    t1=t1_np,
    t2=t2_np,
)

Note

<!-- For more examples and details please refer to our extensive Notebook tutorials here [NBViewer](https://nbviewer.org/github/BrainLesion/tutorials/blob/main/petu/tutorial.ipynb) ([GitHub](https://github.com/BrainLesion/tutorials/blob/main/petu/tutorial.ipynb)). For the best experience open the notebook in Colab. -->

## Citation
Please support our development by citing the following manuscripts:

[Enhancing efficiency in paediatric brain tumour segmentation using a pathologically diverse single-center clinical dataset](https://doi.org/10.48550/arXiv.2507.22152)

@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](https://github.com/BrainLesion/petu/issues).

### Code contributions

Nice to have you on board! Please have a look at our [CONTRIBUTING.md](CONTRIBUTING.md) file.

Sections¶

Getting Started:

Readme

API Reference: