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license: apache-2.0
tags:
- medical-imaging
- brain-segmentation
- 3d-unet
- unet
- mri
- uhf-mri
- contrast-agnostic
- resolution-agnostic
pipeline_tag: image-segmentation
---
# GOUHFI 2.0
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
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