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

Self-supervised Registration and Segmentation of the Ossicles with A Single Ground Truth Label