Image registration is a critical step for robust and accurate atlas-based analyses on brain volumetric changes. This is especially a challenge in brain growth studies in infants between birth and 6 months of life, when both T1w and T2w magnetic resonance images experience time-varying contrast changes. In this study, we compared image registration outcomes via T1w and tissue segmented images derived using a deep learning method. Our results demonstrated superior registration accuracy based on segmentation images, probably due to clear boundaries between tissues types, as well as their time-constant contrasts over development.
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