  Brains with complex distortion of cerebral anatomy present several challenges
to automatic tissue segmentation methods of T1-weighted MR images. First, the
very high variability in the morphology of the tissues can be incompatible with
the prior knowledge embedded within the algorithms. Second, the availability of
MR images of distorted brains is very scarce, so the methods in the literature
have not addressed such cases so far. In this work, we present the first
evaluation of state-of-the-art automatic tissue segmentation pipelines on
T1-weighted images of brains with different severity of congenital or acquired
brain distortion. We compare traditional pipelines and a deep learning model,
i.e. a 3D U-Net trained on normal-appearing brains. Unsurprisingly, traditional
pipelines completely fail to segment the tissues with strong anatomical
distortion. Surprisingly, the 3D U-Net provides useful segmentations that can
be a valuable starting point for manual refinement by
experts/neuroradiologists.
