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.

翻译：人工智能辅助手术因其在提高手术成功率方面的实际影响而引起了医学影像研究界的关注。对于图像引导手术，如人工耳蜗植入术（CIs），术前精确的物体分割可为外科医生提供有价值的信息。近期发表的基于机器学习的图像分割方法通常依赖大量人工预标注的真实标签。然而，数据集的准备是一项费时费力的工作。本文提出一种基于自监督3D-UNet的新型技术，该技术可生成图谱与目标图像之间的稠密变形场，并用于听小骨的图谱导向分割。结果表明，基于Dice相似系数和点到点误差比较，我们的方法优于传统图像分割方法，且能生成更精确的听小骨边界。采用所提方法后，平均Dice系数提升了8.51%。