3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913245684563968 |
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| author | Zhang, Yifu Liu, Zuozhu Feng, Yang Xu, Renjing |
| author_facet | Zhang, Yifu Liu, Zuozhu Feng, Yang Xu, Renjing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_11015 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | 3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images Zhang, Yifu Liu, Zuozhu Feng, Yang Xu, Renjing Image and Video Processing Machine Learning 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. |
| title | 3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2309.11015 |