Self-supervised registration and segmentation on ossicles with a single ground truth label
Yike Zhang, Jack H. Noble · 2023
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.