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.
@article{arxiv.2506.11860,
title = {MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser},
author = {Armina Fani and Mike Doan and Isabelle Le and Alex Fedorov and Malte Hoffmann and Chris Rorden and Sergey Plis},
journal= {arXiv preprint arXiv:2506.11860},
year = {2026}
}