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.
@article{arxiv.2309.11015,
title = {3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images},
author = {Yifu Zhang and Zuozhu Liu and Yang Feng and Renjing Xu},
journal= {arXiv preprint arXiv:2309.11015},
year = {2024}
}