  The Developing Human Connectome Project (dHCP) aims to explore developmental
patterns of the human brain during the perinatal period. An automated
processing pipeline has been developed to extract high-quality cortical
surfaces from structural brain magnetic resonance (MR) images for the dHCP
neonatal dataset. However, the current implementation of the pipeline requires
more than 6.5 hours to process a single MRI scan, making it expensive for
large-scale neuroimaging studies. In this paper, we propose a fast deep
learning (DL) based pipeline for dHCP neonatal cortical surface reconstruction,
incorporating DL-based brain extraction, cortical surface reconstruction and
spherical projection, as well as GPU-accelerated cortical surface inflation and
cortical feature estimation. We introduce a multiscale deformation network to
learn diffeomorphic cortical surface reconstruction end-to-end from T2-weighted
brain MRI. A fast unsupervised spherical mapping approach is integrated to
minimize metric distortions between cortical surfaces and projected spheres.
The entire workflow of our DL-based dHCP pipeline completes within only 24
seconds on a modern GPU, which is nearly 1000 times faster than the original
dHCP pipeline. Manual quality control demonstrates that for 82.5% of the test
samples, our DL-based pipeline produces superior (54.2%) or equal quality
(28.3%) cortical surfaces compared to the original dHCP pipeline.
