Initial push for README.

README.md

@@ -1,3 +1,65 @@
 ---
 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 from 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 are available on GitHub:
+
+https://github.com/mafortin/GOUHFI
+
+## Archival Release
+
+The official archival release 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