arXiv Open Access 2023

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

Yifu Zhang Zuozhu Liu Yang Feng Renjing Xu

Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

eess.IV cs.LG

Penulis (4)

Yifu Zhang

Zuozhu Liu

Yang Feng

Renjing Xu

Format Sitasi

APA MLA BibTeX

Zhang, Y., Liu, Z., Feng, Y., Xu, R. (2023). 3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images. https://arxiv.org/abs/2309.11015

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓