MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908797157507072 |
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| author | Fani, Armina Doan, Mike Le, Isabelle Fedorov, Alex Hoffmann, Malte Rorden, Chris Plis, Sergey |
| author_facet | Fani, Armina Doan, Mike Le, Isabelle Fedorov, Alex Hoffmann, Malte Rorden, Chris Plis, Sergey |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11860 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser Fani, Armina Doan, Mike Le, Isabelle Fedorov, Alex Hoffmann, Malte Rorden, Chris Plis, Sergey Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Neural and Evolutionary Computing 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. |
| title | MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2506.11860 |