MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser
Armina Fani (1), Mike Doan (1), Isabelle Le (1), Alex Fedorov (2), Malte Hoffmann (3), Chris Rorden (4), Sergey Plis (1) ((1) Tri-Institutional Center for Translational Research in Neuroimaging, Data Science (TReNDS), Georgia State University, Georgia Institute of Technology

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…
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Taxonomy
TopicsRobotics and Automated Systems · EEG and Brain-Computer Interfaces · Gaze Tracking and Assistive Technology
