  To achieve fast, robust, and accurate reconstruction of the human cortical
surfaces from 3D magnetic resonance images (MRIs), we develop a novel deep
learning-based framework, referred to as SurfNN, to reconstruct simultaneously
both inner (between white matter and gray matter) and outer (pial) surfaces
from MRIs. Different from existing deep learning-based cortical surface
reconstruction methods that either reconstruct the cortical surfaces separately
or neglect the interdependence between the inner and outer surfaces, SurfNN
reconstructs both the inner and outer cortical surfaces jointly by training a
single network to predict a midthickness surface that lies at the center of the
inner and outer cortical surfaces. The input of SurfNN consists of a 3D MRI and
an initialization of the midthickness surface that is represented both
implicitly as a 3D distance map and explicitly as a triangular mesh with
spherical topology, and its output includes both the inner and outer cortical
surfaces, as well as the midthickness surface. The method has been evaluated on
a large-scale MRI dataset and demonstrated competitive cortical surface
reconstruction performance.
