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Bitewing Radiography Semantic Segmentation Base on Conditional Generative Adversarial Nets.

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Jiang Yun, Tan Ning, Zhang Hai, Peng Tingting

Currently, Segmentation of bitewing radiograpy images is a very challengingtask. The focus of the study is to segment it into caries, enamel, dentin,pulp, crowns, restoration and root canal treatments. The main method ofsemantic segmentation of bitewing radiograpy images at this stage is theU-shaped deep convolution neural network, but its accuracy is low. in order toimprove 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 semanticsegmentation of bitewing radiograpy images. The experimental results show thatthe accuracy of cGAN combined with U-Net is 69.7%, which is 13.3% higher thanthe accuracy of u-shaped deep convolution neural network of 56.4%.

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