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Traditional cortical surface reconstruction is time consuming and limited bythe resolution of brain Magnetic Resonance Imaging (MRI). In this work, weintroduce Pial Neural Network (PialNN), a 3D deep learning framework for pialsurface reconstruction. PialNN is trained end-to-end to deform an initial whitematter surface to a target pial surface by a sequence of learned deformationblocks. A local convolutional operation is incorporated in each block tocapture the multi-scale MRI information of each vertex and its neighborhood.This is fast and memory-efficient, which allows reconstructing a pial surfacemesh with 150k vertices in one second. The performance is evaluated on theHuman Connectome Project (HCP) dataset including T1-weighted MRI scans of 300subjects. The experimental results demonstrate that PialNN reduces thegeometric error of the predicted pial surface by 30% compared tostate-of-the-art deep learning approaches.

PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction