Self-supervised registration and segmentation on ossicles with a single ground truth label

Yike Zhang; Jack H. Noble

doi:10.1117/12.2655653

3 April 2023 Self-supervised registration and segmentation on ossicles with a single ground truth label

Yike Zhang, Jack H. Noble

Proceedings Volume 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling; 124660X (2023) https://doi.org/10.1117/12.2655653
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

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 annotated 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 comparisons. The mean Dice coefficient is improved by 8.51% with our proposed method.

Conference Presentation

Citation Download Citation

Yike Zhang and Jack H. Noble "Self-supervised registration and segmentation on ossicles with a single ground truth label", Proc. SPIE 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 124660X (3 April 2023); https://doi.org/10.1117/12.2655653

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