With a Python 3.10+ environment, you can install gliomoda directly from PyPI:

pip install gliomoda

GlioMODA is trained on BraTS (Brain Tumor Segmentation) preprocessed images. This preprocessing typically includes co-registration to the T1c, skull stripping (brain extraction), and registration to the SRI-24 brain atlas (template).

We recommend using the preprocessing package, part of the BrainLesion Suite, to design custom preprocessing pipelines tailored to your specific needs.

Alternatively, if you have the full set of MRI modalities (T1, T1c, T2, FLAIR), you can use glioma-specific preprocessing functions from the BraTS Orchestrator.

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

from gliomoda import Inferer

inferer = Inferer()

# Save NIfTI files
inferer.infer(
    t1c="path/to/t1c.nii.gz",
    t2f="path/to/t2f.nii.gz",
    t1n="path/to/t1n.nii.gz",
    t2w="path/to/t2w.nii.gz",
    segmentation_file="path/to/segmentation.nii.gz",
)

# Or directly use pre-loaded NumPy data. (Both works as well)
segmentation_np = inferer.infer(
    t1c=t1c_np,
    t2f=t2f_np,
    t1n=t1n_np,
    t2w=t2w_np,
)

Canisius, J., Buchner, J., Rosier, M., Griessmair, M., Peeken, J., Kirschke, J. S., Piraud, M., Bakas, S., Menze, B., Wiestler, B., & Kofler, F. (2025). GlioMODA: Robust Glioma Segmentation in Clinical Routine (p. 2025.11.12.25339968). medRxiv. https://doi.org/10.1101/2025.11.12.25339968

 @article{Canisius_et_al_2025, title={GlioMODA: Robust Glioma Segmentation in Clinical Routine}, rights={© 2025, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/}, ISSN={3067-2007}, url={https://www.medrxiv.org/content/10.1101/2025.11.12.25339968v1}, DOI={10.1101/2025.11.12.25339968}, publisher={medRxiv}, author={Canisius, Julian and Buchner, Josef and Rosier, Marcel and Griessmair, Michael and Peeken, Jan and Kirschke, Jan S. and Piraud, Marie and Bakas, Spyridon and Menze, Bjoern and Wiestler, Benedikt and Kofler, Florian}, year={2025}, month=nov, pages={2025.11.12.25339968}, language={en} }

RuntimeError: 
        An attempt has been made to start a new process before the
        current process has finished its bootstrapping phase.

        This probably means that you are not using fork to start your
        child processes and you have forgotten to use the proper idiom
        in the main module:

            if __name__ == '__main__':
                freeze_support()
                ...

        The "freeze_support()" line can be omitted if the program
        is not going to be frozen to produce an executable.

        To fix this issue, refer to the "Safe importing of main module"
        section in https://docs.python.org/3/library/multiprocessing.html

if __name__ == "__main__":
    inferer = Inferer()
    ...

We welcome all kinds of contributions from the community!

Please open a new issue here.

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