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.
The transformation of biomass to graphene has recently attracted increasing attention because of its high carbon contents and renewable nature. Various applications of biomass-derived graphene are currently sought because of...
A fast redistribution of metal atoms occurs upon mixing the AgxAu38-x and Au38 nanoclusters in solution, as observed by mass spectrometry. Physical separation of AgxAu38-x and Au38 species by a dialysis membrane prohibits the metal migration, which suggests that collisions between the reacting clusters are at the origin of the observation.
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