  The meninges, located between the skull and brain, are composed of three
membrane layers: the pia, the arachnoid, and the dura. Reconstruction of these
layers can aid in studying volume differences between patients with
neurodegenerative diseases and normal aging subjects. In this work, we use
convolutional neural networks (CNNs) to reconstruct surfaces representing
meningeal layer boundaries from magnetic resonance (MR) images. We first use
the CNNs to predict the signed distance functions (SDFs) representing these
surfaces while preserving their anatomical ordering. The marching cubes
algorithm is then used to generate continuous surface representations; both the
subarachnoid space (SAS) and the intracranial volume (ICV) are computed from
these surfaces. The proposed method is compared to a state-of-the-art
deformable model-based reconstruction method, and we show that our method can
reconstruct smoother and more accurate surfaces using less computation time.
Finally, we conduct experiments with volumetric analysis on both subjects with
multiple sclerosis and healthy controls. For healthy and MS subjects, ICVs and
SAS volumes are found to be significantly correlated to sex (p<0.01) and age
(p<0.03) changes, respectively.
