The study of neurodegenerative diseases relies on the reconstruction and
analysis of the brain cortex from magnetic resonance imaging (MRI). Traditional
frameworks for this task like FreeSurfer demand lengthy runtimes, while its
accelerated variant FastSurfer still relies on a voxel-wise segmentation which
is limited by its resolution to capture narrow continuous objects as cortical
surfaces. Having these limitations in mind, we propose DeepCSR, a 3D deep
learning framework for cortical surface reconstruction from MRI. Towards this
end, we train a neural network model with hypercolumn features to predict
implicit surface representations for points in a brain template space. After
training, the cortical surface at a desired level of detail is obtained by
evaluating surface representations at specific coordinates, and subsequently
applying a topology correction algorithm and an isosurface extraction method.
Thanks to the continuous nature of this approach and the efficacy of its
hypercolumn features scheme, DeepCSR efficiently reconstructs cortical surfaces
at high resolution capturing fine details in the cortical folding. Moreover,
DeepCSR is as accurate, more precise, and faster than the widely used
FreeSurfer toolbox and its deep learning powered variant FastSurfer on
reconstructing cortical surfaces from MRI which should facilitate large-scale
medical studies and new healthcare applications.

本文提出了一种基于深度学习的 3D 框架 DeepCSR，用于从 MRI 中重建大脑皮层，并在清晰地捕捉皮层折叠的同时，能够高效地重建皮层表面，比 FreeSurfer 和 FastSurfer 更准确更快。