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Accurate tooth segmentation is an essential step for reconstructing the three-dimensional tooth models used in various clinical applications. In this paper, we propose a convolutional neural network (CNN) based method for fully-automatic tooth segmentation with multi-phase training and preprocessing. For multi-phase training, we defined and used sub-volumes of different sizes to produce stable and fast convergence. To deal with the cone-beam computed tomography (CBCT) images from various CBCT scanners, we used a histogram-based method as a preprocessing step to estimate the average gray density level of the bone and tooth regions. Also, we developed a posterior probability function. Regularizing the CNN models with spatial dropout layers and replacing the convolutional layers with dense convolution blocks further improved the segmentation performance. Experimental results showed that the proposed method compared favorably with existing methods.INDEX TERMS Cone-beam computed tomography, convolutional neural network, network regularization, posterior probability, tooth segmentation.

Transient stability assessment (TSA) based on security region is of great significance to the security of power systems. In this paper, we propose a novel methodology for the assessment of online transient stability margin. Combined with a geographic information system (GIS) and transformation rules, the topology information and pre-fault power flow characteristics can be extracted by 2D computer-vision-based power flow images (CVPFIs). Then, a convolutional neural network (CNN)based comprehensive network is constructed to map the relationship between the steady-state power flow and the generator stability indices under the anticipated contingency set. The network consists of two components: the classification network classifies the input samples into the credibly stable/unstable and uncertain categories, and the prediction network is utilized to further predict the generator stability indices of the categorized samples, which improves the network ability to distinguish between the samples with similar characteristics. The proposed methodology can be used to quickly and quantitatively evaluate the transient stability margin of a power system, and the simulation results validate the effectiveness of the method.

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