A Multi-Stage Framework for 3D Individual Tooth Segmentation in Dental CBCT

Autor:	Wang, Chunshi, Zhao, Bin, Ding, Shuxue
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Cone beam computed tomography (CBCT) is a common way of diagnosing dental related diseases. Accurate segmentation of 3D tooth is of importance for the treatment. Although deep learning based methods have achieved convincing results in medical image processing, they need a large of annotated data for network training, making it very time-consuming in data collection and annotation. Besides, domain shift widely existing in the distribution of data acquired by different devices impacts severely the model generalization. To resolve the problem, we propose a multi-stage framework for 3D tooth segmentation in dental CBCT, which achieves the third place in the "Semi-supervised Teeth Segmentation" 3D (STS-3D) challenge. The experiments on validation set compared with other semi-supervised segmentation methods further indicate the validity of our approach. Comment: Semi-supervised Tooth Segmentation MICCAI 2023 Challenge
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.10433 Zobrazit plný text záznamu View this record from Arxiv