DABS-LS: Deep Atlas-Based Segmentation Using Regional Level Set
  Self-Supervision
Hannah G. Mason Jack H. Noble
Cochlear implants (CIs) are neural prosthetics used to treat patients with
severe-to-profound hearing loss. Patient-specific modeling of CI stimulation of
the auditory nerve fiber (ANFs) can help audiologists improve the CI
programming. These models require localization of the ANFs relative to
surrounding anatomy and the CI. Localization is challenging because the ANFs
are so small they are not directly visible in clinical imaging. In this work,
we hypothesize the position of the ANFs can be accurately inferred from the
location of the internal auditory canal (IAC), which has high contrast in CT,
since the ANFs pass through this canal between the cochlea and the brain.
Inspired by VoxelMorph, in this paper we propose a deep atlas-based IAC
segmentation network. We create a single atlas in which the IAC and ANFs are
pre-localized. Our network is trained to produce deformation fields (DFs)
mapping coordinates from the atlas to new target volumes and that accurately
segment the IAC. We hypothesize that DFs that accurately segment the IAC in
target images will also facilitate accurate atlas-based localization of the
ANFs. As opposed to VoxelMorph, which aims to produce DFs that accurately
register the entire volume, our novel contribution is an entirely
self-supervised training scheme that aims to produce DFs that accurately
segment the target structure. This self-supervision is facilitated using a
regional level set (LS) inspired loss function. We call our method Deep Atlas
Based Segmentation using Level Sets (DABS-LS). Results show that DABS-LS
outperforms VoxelMorph for IAC segmentation. Tests with publicly available
datasets for trachea and kidney segmentation also show significant improvement
in segmentation accuracy, demonstrating the generalizability of the method.