| ---
|
| license: apache-2.0
|
| tags:
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| - medical-imaging
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| - brain-segmentation
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| - 3d-unet
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| - unet
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| - mri
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| - uhf-mri
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| - contrast-agnostic
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| - resolution-agnostic
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| 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:
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|
|
| https://zenodo.org/records/17920473
|
|
|
| ## Paper
|
|
|
| If you use this work, please cite:
|
|
|
| - GOUHFI original publication in Imaging Neuroscience:
|
| ```bibtex
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| @article{fortin2025gouhfi,
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| title={GOUHFI: A novel contrast-and resolution-agnostic segmentation tool for ultra-high-field MRI},
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| 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},
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| journal={Imaging Neuroscience},
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| volume={3},
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| pages={IMAG--a},
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| year={2025}
|
| }
|
| ```
|
|
|
| - Pre-print of GOUHFI 2.0:
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| ```bibtex
|
| @article{fortin2026gouhfi,
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| title={GOUHFI 2.0: A Next-Generation Toolbox for Brain Segmentation and Cortex Parcellation at Ultra-High Field MRI},
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| author={Fortin, Marc-Antoine and Kristoffersen, Anne Louise and Goa, Paal Erik},
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| journal={arXiv preprint arXiv:2601.09006},
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| year={2026}
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| }
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| ```
|
|
|
| ## Intended Use
|
|
|
| This model is intended for research use only.
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|
|
| It is not intended for clinical diagnosis, treatment planning, or medical decision-making without appropriate validation and regulatory approval.
|
|
|
| ## License
|
|
|
| Apache License 2.0
|
|
|
