  AI-assisted surgeries have drawn the attention of the medical image research
community due to their real-world impact on improving surgery success rates.
For image-guided surgeries, such as Cochlear Implants (CIs), accurate object
segmentation can provide useful information for surgeons before an operation.
Recently published image segmentation methods that leverage machine learning
usually rely on a large number of manually predefined ground truth labels.
However, it is a laborious and time-consuming task to prepare the dataset. This
paper presents a novel technique using a self-supervised 3D-UNet that produces
a dense deformation field between an atlas and a target image that can be used
for atlas-based segmentation of the ossicles. Our results show that our method
outperforms traditional image segmentation methods and generates a more
accurate boundary around the ossicles based on Dice similarity coefficient and
point-to-point error comparison. The mean Dice coefficient is improved by 8.51%
with our proposed method.
