Learning Spatiotemporal Probabilistic Atlas of Fetal Brains with Anatomically Constrained Registration Network.

Brain atlases are of fundamental importance for analyzing the dynamic neurodevelopment in fetal brain studies. Since the brain size, shape, and anatomical structures change rapidly during the prenatal period, it is essential to construct a spatiotemporal (4D) atlas equipped with tissue probability maps, which can preserve sharper early brain folding patterns for accurately characterizing dynamic changes in fetal brains and provide tissue prior informations for related tasks, e.g., segmentation, registration, and parcellation. In this work, we propose a novel unsupervised age-conditional learning framework to build temporally continuous fetal brain atlases by incorporating tissue segmentation maps, which outperforms previous traditional atlas construction methods in three aspects. First, our framework enables learning age-conditional deformable templates by leveraging the entire collection. Second, we leverage reliable brain tissue segmentation maps in addition to the low-contrast noisy intensity images to enhance the alignment of individual images. Third, a novel loss function is designed to enforce the similarity between the learned tissue probability map on the atlas and each subject tissue segmentation map after registration, thereby providing extra anatomical consistency supervision for atlas building. Our 4D temporally-continuous fetal brain atlases are constructed based on 82 healthy fetuses from 22 to 32 gestational weeks. Compared with the atlases built by the state-of-the-art algorithms, our atlases preserve more structural details and sharper folding patterns. Together with the learned tissue probability maps, our 4D fetal atlases provide a valuable reference for spatial normalization and analysis of fetal brain development.