A new cascaded fully convolutional neural network for the simultaneous segmentation of parasagittal dural space and arachnoid granulations

The overarching goal of this work is to develop and validate novel deep learning algorithms for segmenting the peri-sinus space, including parasagittal dural (PSD) space, which has been hypothesized to harbor cerebral lymphatic channels, and intra-veinous arachnoid granulations, which has been long-hypothesized as a site a CSF egress, from standard non-contrast anatomical imaging. The new segmentation method is based on cascaded neural networks using non-contrasted 3D T2-weighted MRI; the method is method in a mixed cohort of adults with and without neurodegeneration.