SURFNN: Joint Multiple Cortical Surfaces Reconstruction From 3D Brain MRIs

In order to achieve fast, robust, and accurate reconstruction of the human cortical surfaces from 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 3D 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 image and an initialization 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 neuroimage dataset, ADNI, and demonstrated competitive cortical surface reconstruction performance (~0.2mm average surface distance) compared with alternative deep learning-based methods.