SurfNN: Joint Reconstruction of Multiple Cortical Surfaces from Magnetic Resonance Images
This work addresses the need for fast and accurate cortical surface reconstruction in neuroimaging, though it appears incremental as it builds on existing deep learning methods by focusing on joint prediction.
The paper tackled the problem of reconstructing multiple cortical surfaces from 3D MRI scans by developing SurfNN, a deep learning framework that jointly predicts inner, outer, and midthickness surfaces, achieving competitive performance on a large-scale dataset.
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.