YOLR: an automatic tooth segmentation and detection network

Yanfu Wang; Li Yang; Duyang Wang; Bin Wu

doi:10.1117/12.2636673

10 June 2022 YOLR: an automatic tooth segmentation and detection network

Yanfu Wang, Li Yang, Duyang Wang, Bin Wu

Proceedings Volume 12179, Second International Conference on Medical Imaging and Additive Manufacturing (ICMIAM 2022); 1217915 (2022) https://doi.org/10.1117/12.2636673
Event: Second International Conference on Medical Imaging and Additive Manufacturing (ICMIAM 2022), 2022, Xiamen, China

Abstract

With the improvement of the quality of human life, more and more people pay more attention to dental health. Panoramic CT image is an important method for studying teeth. The existing technology simply classifies or segment the teeth, and cannot accurately segment each tooth independently. The reason is that they did not consider the relationship between tooth type and location. Therefore, this article proposes a method that combines tooth position and tooth type. Mainly by adding the LSTM network to the extracted features to perform feature screening, and then perform classification, detection, and segmentation tasks. Experimental results show that the effective combination of CNN and RNN can accurately detect and segment teeth.

Citation Download Citation

Yanfu Wang, Li Yang, Duyang Wang, and Bin Wu "YOLR: an automatic tooth segmentation and detection network", Proc. SPIE 12179, Second International Conference on Medical Imaging and Additive Manufacturing (ICMIAM 2022), 1217915 (10 June 2022); https://doi.org/10.1117/12.2636673

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