Abstract

Info

Author(s):: ZHAO Shuxu; LUO Qing; WANG Xiaolong; School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Keywords:: Keywords: tooth recognition tooth segmentation Mask R-CNN skip-connection structure SE module

Abstract:: Abstract: In view of the fact that the information extracted from dental panoramic X-ray image by current research methods is relatively fewer, and the problem of without considering the fusion extraction of classification information and shape and position information of teeth, an instance segmentation method is proposed to realize the teeth recognition and segmentation simultaneously. The segmentation branch of Mask R-CNN instance segmentation model is improved by fusing skip-connection structure and SE (Squeeze and Excitation) module. Two teeth coding methods of tooth function and FDI tooth position were used for the experimental simulation on 400 dental panoramic X-ray images. The experimental results show that compared with other models, the improved model can effectively complete teeth classification and segmentation at the same time, realize the fusion extraction of tooth category, shape and position, and overcome the problem of insufficient semantic information extraction in the segmentation branch of Mask R-CNN instance segmentation model.