Accurate representation of tooth position is extremely important in treatment. 3D dental image segmentation is a widely used method, however labelled 3D dental datasets are a scarce resource, leading to the problem of small samples that this task faces in many cases. To this end, we address this problem with a pretrained SAM and propose a novel 3D-U-SAM network for 3D dental image segmentation. Specifically, in order to solve the problem of using 2D pre-trained weights on 3D datasets, we adopted a convolution approximation method; in order to retain more details, we designed skip connections to fuse features at all levels with reference to U-Net. The effectiveness of the proposed method is demonstrated in ablation experiments, comparison experiments, and sample size experiments.

翻译：牙齿位置的精确表示在治疗中极为重要。三维牙齿图像分割是一种广泛应用的方法，然而标注的三维牙齿数据集资源稀缺，导致该任务在许多情况下面临小样本问题。为此，我们利用预训练的SAM来解决这一问题，并提出了一种新颖的3D-U-SAM网络用于三维牙齿图像分割。具体而言，为解决在三维数据集上使用二维预训练权重的问题，我们采用了一种卷积近似方法；为保留更多细节，我们参考U-Net设计了跳跃连接以融合所有层级的特征。消融实验、对比实验及样本量实验均证明了所提方法的有效性。