Abstract
The reconstruction of cortical surfaces from brain magnetic resonance imaging
(MRI) scans is essential for quantitative analyses of cortical thickness and
sulcal morphology. Although traditional and deep learning-based algorithmic
pipelines exist for this purpose, they have two major drawbacks: lengthy
runtimes of multiple hours (traditional) or intricate post-processing, such as
mesh extraction and topology correction (deep learning-based). In this work, we
address both of these issues and propose Vox2Cortex, a deep learning-based
algorithm that directly yields topologically correct, three-dimensional meshes
of the boundaries of the cortex. Vox2Cortex leverages convolutional and graph
convolutional neural networks to deform an initial template to the densely
folded geometry of the cortex represented by an input MRI scan. We show in
extensive experiments on three brain MRI datasets that our meshes are as
accurate as the ones reconstructed by state-of-the-art methods in the field,
without the need for time- and resource-intensive post-processing. To
accurately reconstruct the tightly folded cortex, we work with meshes
containing about 168,000 vertices at test time, scaling deep explicit
reconstruction methods to a new level.
Publication
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)