MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser
Journal:
arXiv
Published Date:
Jun 13, 2025
Abstract
We developed MindGrab, a parameter- and memory-efficient deep
fully-convolutional model for volumetric skull-stripping in head images of any
modality. Its architecture, informed by a spectral interpretation of dilated
convolutions, was trained exclusively on modality-agnostic synthetic data.
MindGrab was evaluated on a retrospective dataset of 606 multimodal adult-brain
scans (T1, T2, DWI, MRA, PDw MRI, EPI, CT, PET) sourced from the SynthStrip
dataset. Performance was benchmarked against SynthStrip, ROBEX, and BET using
Dice scores, with Wilcoxon signed-rank significance tests. MindGrab achieved a
mean Dice score of 95.9 with standard deviation (SD) 1.6 across modalities,
significantly outperforming classical methods (ROBEX: 89.1 SD 7.7, P < 0.05;
BET: 85.2 SD 14.4, P < 0.05). Compared to SynthStrip (96.5 SD 1.1, P=0.0352),
MindGrab delivered equivalent or superior performance in nearly half of the
tested scenarios, with minor differences (<3% Dice) in the others. MindGrab
utilized 95% fewer parameters (146,237 vs. 2,566,561) than SynthStrip. This
efficiency yielded at least 2x faster inference, 50% lower memory usage on
GPUs, and enabled exceptional performance (e.g., 10-30x speedup, and up to 30x
memory reduction) and accessibility on a wider range of hardware, including
systems without high-end GPUs. MindGrab delivers state-of-the-art accuracy with
dramatically lower resource demands, supported in brainchop-cli
(https://pypi.org/project/brainchop/) and at brainchop.org.
