Construction of Longitudinally Consistent 4D Infant Cerebellum Atlases Based on Deep Learning.

ABSTRACT: Longitudinal infant dedicated cerebellum atlases play a fundamental role in characterizing and understanding the dynamic cerebellum development during infancy. However, due to the limited spatial resolution, low tissue contrast, tiny folding structures, and rapid growth of the cerebellum during this stage, it is challenging to build such atlases while preserving clear folding details. Furthermore, the existing atlas construction methods typically independently build discrete atlases based on samples for each age group without considering the within-subject temporal consistency, which is critical for large-scale longitudinal studies. To fill this gap, we propose an age-conditional multi-stage learning framework to construct longitudinally consistent 4D infant cerebellum atlases. Specificall

SUBMITTER: Chen L 

PROVIDER: S-EPMC8817766 | biostudies-literature | 2021 Sep-Oct

REPOSITORIES: biostudies-literature