  The reconstruction of cerebral cortex surfaces from brain MRI scans is
instrumental for the analysis of brain morphology and the detection of cortical
thinning in neurodegenerative diseases like Alzheimer's disease (AD). Moreover,
for a fine-grained analysis of atrophy patterns, the parcellation of the
cortical surfaces into individual brain regions is required. For the former
task, powerful deep learning approaches, which provide highly accurate brain
surfaces of tissue boundaries from input MRI scans in seconds, have recently
been proposed. However, these methods do not come with the ability to provide a
parcellation of the reconstructed surfaces. Instead, separate
brain-parcellation methods have been developed, which typically consider the
cortical surfaces as given, often computed beforehand with FreeSurfer. In this
work, we propose two options, one based on a graph classification branch and
another based on a novel generic 3D reconstruction loss, to augment
template-deformation algorithms such that the surface meshes directly come with
an atlas-based brain parcellation. By combining both options with two of the
latest cortical surface reconstruction algorithms, we attain highly accurate
parcellations with a Dice score of 90.2 (graph classification branch) and 90.4
(novel reconstruction loss) together with state-of-the-art surfaces.

Neurons and Cognition;Machine Learning