  Traditional neuroimage analysis pipelines involve computationally intensive,
time-consuming optimization steps, and thus, do not scale well to large cohort
studies with thousands or tens of thousands of individuals. In this work we
propose a fast and accurate deep learning based neuroimaging pipeline for the
automated processing of structural human brain MRI scans, replicating
FreeSurfer's anatomical segmentation including surface reconstruction and
cortical parcellation. To this end, we introduce an advanced deep learning
architecture capable of whole brain segmentation into 95 classes. The network
architecture incorporates local and global competition via competitive dense
blocks and competitive skip pathways, as well as multi-slice information
aggregation that specifically tailor network performance towards accurate
segmentation of both cortical and sub-cortical structures. Further, we perform
fast cortical surface reconstruction and thickness analysis by introducing a
spectral spherical embedding and by directly mapping the cortical labels from
the image to the surface. This approach provides a full FreeSurfer alternative
for volumetric analysis (in under 1 minute) and surface-based thickness
analysis (within only around 1h runtime). For sustainability of this approach
we perform extensive validation: we assert high segmentation accuracy on
several unseen datasets, measure generalizability and demonstrate increased
test-retest reliability, and high sensitivity to group differences in dementia.
