  Currently, Segmentation of bitewing radiograpy images is a very challenging
task. The focus of the study is to segment it into caries, enamel, dentin,
pulp, crowns, restoration and root canal treatments. The main method of
semantic segmentation of bitewing radiograpy images at this stage is the
U-shaped deep convolution neural network, but its accuracy is low. in order to
improve the accuracy of semantic segmentation of bitewing radiograpy images,
this paper proposes the use of Conditional Generative Adversarial network
(cGAN) combined with U-shaped network structure (U-Net) approach to semantic
segmentation of bitewing radiograpy images. The experimental results show that
the accuracy of cGAN combined with U-Net is 69.7%, which is 13.3% higher than
the accuracy of u-shaped deep convolution neural network of 56.4%.
