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