  Two segmentation methods, one atlas-based and one neural-network-based, were
compared to see how well they can each automatically segment the brain stem and
cerebellum in Displacement Encoding with Stimulated Echoes Magnetic Resonance
Imaging (DENSE-MRI) data. The segmentation is a pre-requisite for estimating
the average displacements in these regions, which have recently been proposed
as biomarkers in the diagnosis of Chiari Malformation type I (CMI). In
numerical experiments, the segmentations of both methods were similar to manual
segmentations provided by trained experts. It was found that, overall, the
neural-network-based method alone produced more accurate segmentations than the
atlas-based method did alone, but that a combination of the two methods -- in
which the atlas-based method is used for the segmentation of the brain stem and
the neural-network is used for the segmentation of the cerebellum -- may be the
most successful.
