  We propose an atlas-based method to segment the intracochlear anatomy (ICA)
in the post-implantation CT (Post-CT) images of cochlear implant (CI)
recipients that preserves the point-to-point correspondence between the meshes
in the atlas and the segmented volumes. To solve this problem, which is
challenging because of the strong artifacts produced by the implant, we use a
pair of co-trained deep networks that generate dense deformation fields (DDFs)
in opposite directions. One network is tasked with registering an atlas image
to the Post-CT images and the other network is tasked with registering the
Post-CT images to the atlas image. The networks are trained using loss
functions based on voxel-wise labels, image content, fiducial registration
error, and cycle-consistency constraint. The segmentation of the ICA in the
Post-CT images is subsequently obtained by transferring the predefined
segmentation meshes of the ICA in the atlas image to the Post-CT images using
the corresponding DDFs generated by the trained registration networks. Our
model can learn the underlying geometric features of the ICA even though they
are obscured by the metal artifacts. We show that our end-to-end network
produces results that are comparable to the current state of the art (SOTA)
that relies on a two-steps approach that first uses conditional generative
adversarial networks to synthesize artifact-free images from the Post-CT images
and then uses an active shape model-based method to segment the ICA in the
synthetic images. Our method requires a fraction of the time needed by the
SOTA, which is important for end-user acceptance.
