MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser

TL;DR

MindGrab is a lightweight, fast, and accurate skull stripping tool for neuroimaging that operates efficiently on resource-limited devices and can be used via command line or in-browser, broadening accessibility.

Contribution

We introduce MindGrab, a novel spectral convolution-based model that achieves state-of-the-art accuracy with minimal deployment complexity and resource requirements.

Findings

Achieves a mean Dice score of 95.9 with SD 1.6 across datasets.

Up to 40-fold speed improvements over traditional methods.

Operates effectively in resource-constrained environments, including in-browser execution.

Abstract

Deployment complexity and specialized hardware requirements hinder the adoption of deep learning models in neuroimaging. We present MindGrab, a lightweight, fully convolutional model for volumetric skull stripping across all imaging modalities. MindGrab's architecture is designed from first principles using a spectral interpretation of dilated convolutions, and demonstrates state-of-the-art performance (mean Dice score across datasets and modalities: 95.9 with SD 1.6), with up to 40-fold speedups and substantially lower memory demands compared to established methods. Its minimal footprint allows for fast, full-volume processing in resource-constrained environments, including direct in-browser execution. MindGrab is delivered via the BrainChop platform as both a simple command-line tool (pip install brainchop) and a zero-installation web application (brainchop.org). By removing traditional deployment barriers without sacrificing accuracy, MindGrab makes state-of-the-art neuroimaging analysis broadly accessible.