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Accurate segmentation of teeth in dental imaging is a principal element in computer-aided design (CAD) in modern dentistry. In this paper, we present a new framework based on deep learning models for segmenting tooth instances in 3D point cloud data of an intra-oral scan (IOS). At high level, the proposed framework, called Mask-MCNet, has analogy to the Mask R-CNN, which gives high performance on 2D images. However, the proposed framework is designed for the challenging task of instance segmentation of point cloud data from surface meshes. By employing the Monte Carlo Convolutional Network (MCCNet), the Mask-MCNet distributes the information from the processed 3D surface points into the entire void space (e.g. inside the objects). Consequently, the model is able to localize each object instance by predicting its 3D bounding box and simultaneously segmenting all the points inside each box. The experiments show that our Mask-MCNet outperforms state-of-the-art for IOS segmentation by achieving 98% IoU score.

This paper introduces an integrated temporal-causal model for dynamics in social networks addressing the contagion principle by which states are affected mutually, and both the homophily principle and the more-becomes-more principle by which connections are adapted over time. The integrated model has been evaluated in three different manners: by simulation experiments, by verification based on mathematical analysis, and by validation against an empirical data set.