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.

翻译：为了实现从三维磁共振图像中快速、稳健且精确地重建人脑皮层表面，我们开发了一种新型深度学习框架，称为SurfNN，用于同时重建磁共振图像中的内层（白质与灰质之间）和外层（软脑膜）表面。与现有基于深度学习的皮层表面重建方法不同——这些方法要么分别重建皮层表面，要么忽视内外表面之间的相互依赖性——SurfNN通过训练单一网络预测位于内外皮层表面中心的中间厚度表面，从而联合重建内外皮层表面。SurfNN的输入包括一个三维磁共振图像以及一个中间厚度表面的初始化表示，该初始化既以三维距离图的隐式形式表示，也以具有球形拓扑的三角网格的显式形式表示；其输出包括内外皮层表面以及中间厚度表面。该方法已在大型磁共振图像数据集上进行了评估，并展示了具有竞争力的皮层表面重建性能。