Bitewing Radiography Semantic Segmentation Base on Conditional Generative Adversarial Nets
This work addresses a domain-specific challenge in dental imaging by improving segmentation accuracy for caries, enamel, dentin, pulp, crowns, restoration, and root canal treatments, representing an incremental advancement over existing methods.
The paper tackled the problem of low accuracy in semantic segmentation of bitewing radiography images by proposing a method combining Conditional Generative Adversarial Networks (cGAN) with U-Net, achieving an accuracy of 69.7%, which is 13.3% higher than the baseline U-shaped deep convolution neural network at 56.4%.
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%.