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所属单位：机电学院
发表刊物：IEEE Access
摘要：To solve the problem of low efficiency, the complexity of the interactive operation, and the high degree of manual intervention in existing methods, we propose a novel approach based on the sparse voxel octree and 3D convolution neural networks (CNNs) for segmenting and classifying tooth types on the 3D dental models. First, the tooth classification method capitalized on the two-level hierarchical feature learning is proposed to solve the misclassification problem in highly similar tooth categories. Second, we exploit an improved three-level hierarchical segmentation method based on the deep convolution features to conduct segmentation of teeth-gingiva and inter-teeth, respectively, and the conditional random field model is used to refine the boundary of the gingival margin and the inter-teeth fusion region. The experimental results show that the classification accuracy in Level 1 network is 95.96%, the average classification accuracy in Level 2 network is 88.06%, and the accuracy of tooth segmentation is 89.81%. Compared with the existing state-of-the-art methods, the proposed method has higher accuracy and universality, and it has great application potential in the computer-assisted orthodontic treatment diagnosis. © 2013 IEEE.
是否译文：否
发表时间：2019-01-01
合写作者：Tian, Sukun,Zhang, Bei,张蓓,Yuan, Fulai,Yu, Qing,程筱胜
通讯作者：Tian, Sukun,戴宁