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The study of neurodegenerative diseases relies on the reconstruction andanalysis of the brain cortex from magnetic resonance imaging (MRI). Traditionalframeworks for this task like FreeSurfer demand lengthy runtimes, while itsaccelerated variant FastSurfer still relies on a voxel-wise segmentation whichis limited by its resolution to capture narrow continuous objects as corticalsurfaces. Having these limitations in mind, we propose DeepCSR, a 3D deeplearning framework for cortical surface reconstruction from MRI. Towards thisend, we train a neural network model with hypercolumn features to predictimplicit surface representations for points in a brain template space. Aftertraining, the cortical surface at a desired level of detail is obtained byevaluating surface representations at specific coordinates, and subsequentlyapplying a topology correction algorithm and an isosurface extraction method.Thanks to the continuous nature of this approach and the efficacy of itshypercolumn features scheme, DeepCSR efficiently reconstructs cortical surfacesat high resolution capturing fine details in the cortical folding. Moreover,DeepCSR is as accurate, more precise, and faster than the widely usedFreeSurfer toolbox and its deep learning powered variant FastSurfer onreconstructing cortical surfaces from MRI which should facilitate large-scalemedical studies and new healthcare applications.

DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction