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Abstract #2686

SynthStrip: skull stripping for any brain image

Andrew Hoopes1, Jocelyn S. Mora1, Adrian Dalca1,2,3, Bruce Fischl*1,2,3, and Malte Hoffmann*1,2
1Martinos Center for Biomedical Imaging, Boston, MA, United States, 2Department of Radiology, Harvard Medical School, Boston, MA, United States, 3Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, United States


The removal of non-brain signal from MR data is an integral component of neuroimaging streams. However, popular skull-stripping utilities are typically tailored to isotropic T1-weighted scans and tend to fail, sometimes catastrophically, on images with other MRI contrasts or stack-of-slices acquisitions that are common in the clinic. We propose SynthStrip, a flexible tool that produces highly accurate brain masks across a landscape of neuroimaging data with widely varying contrast and resolution. We implement our method by leveraging anatomical label maps to synthesize a broad set of training images, optimizing a robust convolutional network agnostic to MRI contrast and acquisition scheme.

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