This repository hosts the model weights for GOUHFI 2.0, a 3D U-Net-based deep learning framework for brain segmentation, cortical parcellation and volumetry measurements using Magnetic Resonance Images (MRI) of any contrast, resolution or field strength.

Source Code

For the full source code, preprocessing pipeline, training scripts, and inference instructions, please visit the official repository available on GitHub:

https://github.com/mafortin/GOUHFI

Archival Release

The official archival release of the trained model weights is available on Zenodo:

https://zenodo.org/records/17920473

Paper

If you use this work, please cite:

GOUHFI original publication in Imaging Neuroscience:

bibtex

@article{fortin2025gouhfi,
  title={GOUHFI: A novel contrast-and resolution-agnostic segmentation tool for ultra-high-field MRI},
  author={Fortin, Marc-Antoine and Kristoffersen, Anne Louise and Larsen, Michael Staff and Lamalle, Laurent and Stirnberg, R{\"u}diger and Goa, P{\aa}l Erik},
  journal={Imaging Neuroscience},
  volume={3},
  pages={IMAG--a},
  year={2025}
}

Pre-print of GOUHFI 2.0:

bibtex

@article{fortin2026gouhfi,
  title={GOUHFI 2.0: A Next-Generation Toolbox for Brain Segmentation and Cortex Parcellation at Ultra-High Field MRI},
  author={Fortin, Marc-Antoine and Kristoffersen, Anne Louise and Goa, Paal Erik},
  journal={arXiv preprint arXiv:2601.09006},
  year={2026}
}

Intended Use

This model is intended for research use only.

It is not intended for clinical diagnosis, treatment planning, or medical decision-making without appropriate validation and regulatory approval.

License

Apache License 2.0

mafortin/GOUHFI2p0

GOUHFI 2.0

Source Code

Archival Release

Paper

Intended Use

License

mafortin/
GOUHFI2p0