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arXiv · 2506.11860

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

Armina Fani·Mike Doan·Isabelle Le·Alex Fedorov·Malte Hoffmann·Chris Rorden·Sergey Plis
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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.

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Armina Fani, Mike Doan, Isabelle Le, Alex Fedorov, Malte Hoffmann, Chris Rorden, Sergey Plis. 2026-01-29. MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser. https://arxiv.org/abs/2506.11860

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