  The spinal cord (SC), which conveys information between the brain and the
peripheral nervous system, plays a key role in various neurological disorders
such as multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS), in
which both gray matter (GM) and white matter (WM) may be impaired. While
automated methods for WM/GM segmentation are now largely available, these
techniques, developed for conventional systems (3T or lower) do not necessarily
perform well on 7T MRI data, which feature finer details, contrasts, but also
different artifacts or signal dropout.
  The primary goal of this study is thus to propose a new deep learning model
that allows robust SC/GM multi-class segmentation based on ultra-high
resolution 7T T2*-w MR images. The second objective is to highlight the
relevance of implementing a specific data augmentation (DA) strategy, in
particular to generate a generic model that could be used for multi-center
studies at 7T.
