3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images

• URL: http://arxiv.org/abs/2309.11015v3
• Date: Wed, 28 Feb 2024 04:35:38 GMT
• Title: 3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images
• Authors: Yifu Zhang and Zuozhu Liu and Yang Feng and Renjing Xu
• Abstract summary: We propose a novel 3D-U-SAM network for 3D dental image segmentation. In order to solve the problem of using 2D pre-trained weights on 3D datasets, we adopted a convolution approximation method. The effectiveness of the proposed method is demonstrated in ablation experiments, comparison experiments, and sample size experiments.
• Score: 22.86724024199165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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