Machine learning research on medical images is becoming popular as advanced imaging technologies and equipment in medicine become more and more available. Dental Cone-beam Computed Tomography (Dental CBCT), a frequently-used visualization tool for oral diagnosis, provides valuable three-dimensional information, whose development for automation of Dental CBCT analysis, on the other hand, is relatively preliminary. Generally, there are three important characteristics for analyzing Dental CBCT with noisy labels and limited labeled sample size, and availability of oral medicine knowledge. Based on those characteristics, we develop an image segmentation method for Dental CBCT by integrating domain knowledge into deep U-Net for the 3D segmentation. Finally, depending on whether the knowledge can be decomposed into each pixel, the knowledge constraints are classified into two types: separable and non-separable constraints. All knowledge constraints can be represented as a posterior regularization term and solved in different ways in accordance with related types. For separable constraints, the mean-field theory is employed to solve an optimization problem with the independence assumption about the distributions of output variables on each pixel. For non-separable constraints, we propose to combine the importance sampling based approach and the stochastic optimization algorithm. Finally, we propose to formulate the domain knowledge to the learning stage to improve the accuracy and efficiency of automation of Dental CBCT segmentation. Finally, we will apply the proposed methods into the real datasets collected and manually labeled by the doctors at the University of Pennsylvania.