Mindgrab

Files

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")

--- 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](https://github.com/neuroneural/brainchop) project. It takes 256^3 conformed T1 volumes and produces a binary brain mask. The checkpoint runs entirely in [tinygrad](https://github.com/tinygrad/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](https://huggingface.co/papers/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](https://brainchop.org/) under the name "🪓🧠 omnimodal Skull Stripping" ## Usage ```sh uv pip install hf brainchop ``` ```python 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") ```