Abstract
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.
Publication
Machine Learning in Clinical Neuroimaging