| license: cc-by-nc-sa-4.0 | |
| tags: | |
| - medical-imaging | |
| - mri | |
| - segmentation | |
| - neuroscience | |
| - unetr | |
| # GRACE — Whole-Head MRI Segmentation | |
| GRACE is a UNETR-based model for automated whole-head MRI segmentation into 12 tissue classes, developed by the SMILE Lab. | |
| ## Variants | |
| - `grace_native.pth` — operates in native MRI space | |
| - `grace_fs.pth` — operates in FreeSurfer conformed space (256³ @ 1mm isotropic) | |
| ## Tissue Classes | |
| See `labels.json` for the full 12-class label map. | |
| ## Usage | |
| Install the CROWN CLI and download this model: | |
| ```bash | |
| pip install crown-cli | |
| crown models download grace-native | |
| crown segment input.nii.gz --model grace-native | |
| ``` | |
| --- | |
| Citation | |
| Stolte, S. E., Indahlastari, A., Chen, J., Albizu, A., Dunn, A., Pedersen, S., See, K. B., Woods, A. J., & Fang, R. (2024). Precise and Rapid Whole-Head Segmentation from Magnetic Resonance Images of Older Adults using Deep Learning. Imaging neuroscience (Cambridge, Mass.), 2, imag-2-00090. https://doi.org/10.1162/imag_a_00090 | |
| --- | |
| Terms | |
| By downloading these weights you agree to use them for non-commercial research purposes only. Redistribution of the weights is not permitted. | |
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