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.

翻译：部署的复杂性及专用硬件需求阻碍了深度学习模型在神经影像学领域的广泛应用。本文提出MindGrab，一种轻量级全卷积模型，适用于所有成像模态的容积颅骨剥离任务。MindGrab的架构基于对空洞卷积的谱解释从第一性原理设计而成，在多个数据集和模态上实现了最先进的性能（平均Dice分数：95.9，标准差：1.6），与现有方法相比速度提升最高达40倍且内存需求显著降低。其极小的计算占用使其能够在资源受限的环境中进行快速的完整容积处理，包括直接在浏览器中执行。MindGrab通过BrainChop平台发布，既可作为简单的命令行工具（pip install brainchop）使用，也可作为零安装的Web应用程序（brainchop.org）访问。MindGrab在保持精度的同时消除了传统部署障碍，使得最先进的神经影像分析技术得以广泛普及。