  The automatic segmentation of perinatal brain structures in magnetic
resonance imaging (MRI) is of utmost importance for the study of brain growth
and related complications. While different methods exist for adult and
pediatric MRI data, there is a lack for automatic tools for the analysis of
perinatal imaging. In this work, a new pipeline for fetal and neonatal
segmentation has been developed. We also report the creation of two new fetal
atlases, and their use within the pipeline for atlas-based segmentation, based
on novel registration methods. The pipeline is also able to extract cortical
and pial surfaces and compute features, such as curvature, thickness, sulcal
depth, and local gyrification index. Results show that the introduction of the
new templates together with our segmentation strategy leads to accurate results
when compared to expert annotations, as well as better performances when
compared to a reference pipeline (developing Human Connectome Project (dHCP)),
for both early and late-onset fetal brains.
