A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation

Modeling and analysis of MR images of the developing human brain is a challenge due to rapid changes in brain morphology and morphometry. We present an approach to the construction of a spatiotemporal atlas of the fetal brain with temporal models of MR intensity, tissue probability and shape changes. This spatiotemporal model is created from a set of reconstructed MR images of fetal subjects with different gestational ages. Groupwise registration of manual segmentations and voxelwise nonlinear modeling allow us to capture the appearance, disappearance and spatial variation of brain structures over time. Applying this model to atlas-based segmentation, we generate age-specific MR templates and tissue probability maps and use them to initialize automatic tissue delineation in new MR images. The choice of model parameters and the final performance are evaluated using clinical MR scans of young fetuses with gestational ages ranging from 20.57 to 24.71 weeks. Experimental results indicate that quadratic temporal models can correctly capture growth-related changes in the fetal brain anatomy and provide improvement in accuracy of atlas-based tissue segmentation.

Research highlights
► Spatiotemporal atlas of the fetal brain created from a set of manual segmentations.
► Atlas includes developing GM and WM as well as transient tissue types such as GMAT.
► MR intensity, tissue probabilities and shape changes are modeled using temporal polynomials.
► Age-specific MR templates and tissue density maps are generated from spatiotemporal atlas.
► Quadratic temporal models provide improved age-specific priors for atlas-based EM tissue labeling.