In recent years, with the deepening of brain science research, mouse magnetic resonance microscopy (MRM) for neuroimaging research has gradually become a main research interest. Brain segmentation is an essential techniques for investigating the brain morphometry, while the traditional approach to segment a given brain involves the manual delineation of the ROIs by an expert. This practice can be slow and unscalable. Although automatic atlas-based segmentation approaches have been developed and validated for the human brain MRI, there is limited work for the mouse brain MRM. This paper combined optimized image registration and multi-atlas model for mouse brain segmentation. The results showed that multiple atlases with optimized geodesic-SyN can best improve the segmentation accuracy in the mouse brain, and registration algorithm plays important role in performance improvement.
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