The human cerebral cortex is one of the most complicated structures in the body. It has a highly convoluted
structure with much of the cortical sheet buried in sulci. Based on cytoarchitectural and functional imaging
studies, it is possible to segment the cerebral cortex into several subregions. While it is only possible to differentiate
the true anatomical subregions based on cytoarchitecture, the surface morphometry aligns closely with the
underlying cytoarchitecture and provides features that allow the surface of the cortex to be parcellated based on
the sulcal and gyral patterns that are readily visible on the MR images.
We have developed a fully automated pipeline for the generation and registration of cortical surfaces in
the spherical domain. The pipeline initiates with the BRAINS AutoWorkup pipeline. Subsequently, topology
correction and surface generation is performed to generate a genus zero surface and mapped to a sphere. Several
surface features are then calculated to drive the registration between the atlas surface and other datasets. A
spherical diffeomorphic demons algorithm is used to co-register an atlas surface onto a subject surface.
A lobar based atlas of the cerebral cortex was created from a manual parcellation of the cortex. The atlas
surface was then co-registered to five additional subjects using a spherical diffeomorphic demons algorithm. The
labels from the atlas surface were warped on the subject surface and compared to the manual raters. The average
Dice overlap index was 0.89 across all regions.
The cerebral cortex is a highly convoluted anatomical structure. The folding pattern defined by sulci and gyri is a
complex pattern that is very heterogeneous across subjects. The heterogeneity across subjects has made the automated
labeling of this structure into its constituent components a challenge to the field of neuroimaging. One way to approach
this problem is to conformally map the surface to another representation such as a plane or sphere. Conformal mapping
of the surface requires that surface to be topologically correct. However, noise and partial volume artifacts in the MR
images frequently cause holes or handles to exist in the surface that must be removed. Topology correction techniques
have been proposed that operate on the cortical surface, the original image data, and hybrid methods have been proposed.
This paper presents an experimental assessment of two different topology correction methods. The first approach is
based on modification of 3D voxel data. The second method is a hybrid approach that determines the location of defects
from the surface representation while repairing the surface by modifying the underlying image data. These methods have
been applied to 10 brains, and a comparison is made among them. In addition, detailed statistics are given based on the
voxel correction method.
Based on these 10 MRI datasets, we have found the hybrid method incapable of correcting the cortical surface
appropriately when a handles and holes exist in close proximity. In several cases, holes in the anatomical surface were
labeled as handles thus resulting in discontinuities in the folding pattern. The image-based approach in this study was
found to correct the topology in all ten cases within a reasonable time. Furthermore, the distance between the original
and corrected surfaces, thickness of brain cortex, curvatures and surface areas are provided as assessments of the
approach based on our datasets.