Application study on the recognition of oral obstructed tooth images using semantic segmentation

Yunfei Wu; Zixuan Li; Jianqing Wang; Mengting He; Wen Zhou; Pengfei Lin

doi:10.1117/12.2687389

28 August 2023 Application study on the recognition of oral obstructed tooth images using semantic segmentation

Yunfei Wu, Zixuan Li, Jianqing Wang, Mengting He, Wen Zhou, Pengfei Lin

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 127242K (2023) https://doi.org/10.1117/12.2687389
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

Oral obstructive teeth are abnormal tooth development due to dysplasia or pressure on surrounding teeth and usually need to be removed during oral surgery. Prior to surgery, the surgeon needs to perform diagnostic imaging to determine the location and shape of the blocked tooth and to plan the surgical plan. Traditional diagnostic imaging methods mainly use radiographs and CT images, but these methods require manual measurement and analysis, which are complex and prone to errors. There is a fast development in Computer Vision and Deep Learning, semantic segmentation recognition of oral obstructive tooth images has become a new solution. In this paper, we will study the semantic segmentation recognition of oral obstructive dental images and propose a deep learning based semantic segmentation method for oral obstructive dental images.

Citation Download Citation

Yunfei Wu, Zixuan Li, Jianqing Wang, Mengting He, Wen Zhou, and Pengfei Lin "Application study on the recognition of oral obstructed tooth images using semantic segmentation", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 127242K (28 August 2023); https://doi.org/10.1117/12.2687389

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KEYWORDS

Image segmentation

Teeth

Semantics

Deep learning

Education and training

Data modeling

Image fusion

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