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--- |
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language: |
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- en |
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tags: |
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- image-segmentation |
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- medical-imaging |
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- brain-mri |
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- tinygrad |
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- skull-stripping |
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license: mit |
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pipeline_tag: image-segmentation |
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--- |
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# MindGrab (BrainChop MeshNet) |
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MindGrab is a MeshNet-based skull-stripping model from the |
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[BrainChop](https://github.com/neuroneural/brainchop) project. It takes 256^3 |
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conformed T1 volumes and produces a binary brain mask. The checkpoint runs |
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entirely in [tinygrad](https://github.com/tinygrad/tinygrad) and powers the |
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in-browser BrainChop demos (WebGPU/WebGL). |
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## Reference |
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If you use MindGrab in academic work, please cite the Hugging Face Papers entry |
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[2506.11860](https://huggingface.co/papers/2506.11860), which documents this |
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release and its evaluation context. |
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## Files |
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| File | Description | |
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|------ |-------------| |
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| `model.json` | MeshNet architecture definition (in/out channels, kernel sizes, bias, dropout flags). | |
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| `layers.json` | Optional Layer configs | |
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| `model.pth` | FP32 PyTorch checkpoint | |
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Optionally, you can also run the model through the official frontend at |
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[brainchop.org](https://brainchop.org/) under the name "🪓🧠 omnimodal Skull |
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Stripping" |
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## Usage |
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```sh |
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uv pip install hf brainchop |
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``` |
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```python |
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from pathlib import Path |
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from huggingface_hub import hf_hub_download |
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from brainchop import load, save, api |
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model_dir = Path(hf_hub_download("neuroneural/mindgrab", "model.json")).parent |
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hf_hub_download(repo_id, "model.pth") |
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vol = load("t1_crop.nii.gz") |
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mask = api.segment(vol, str(model_dir)) |
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save(mask, "mindgrab_mask.nii.gz") |
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``` |
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