A lifespan-generalizable skull-stripping model for magnetic resonance images that leverages prior knowledge from brain atlases.

ABSTRACT: In magnetic resonance imaging of the brain, an imaging-preprocessing step removes the skull and other non-brain tissue from the images. But methods for such a skull-stripping process often struggle with large data heterogeneity across medical sites and with dynamic changes in tissue contrast across lifespans. Here we report a skull-stripping model for magnetic resonance images that generalizes across lifespans by leveraging personalized priors from brain atlases. The model consists of a brain extraction module that provides an initial estimation of the brain tissue on an image, and a registration module that derives a personalized prior from an age-specific atlas. The model is substantially more accurate than state-of-the-art skull-stripping methods, as we show with a large and diverse dat

SUBMITTER: Wang L 

PROVIDER: S-EPMC12360181 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature