Bitewing Radiography Semantic Segmentation Base on Conditional Generative Adversarial Nets

TL;DR

This paper introduces a novel approach combining Conditional Generative Adversarial Networks with U-Net for improved semantic segmentation of bitewing radiography images, significantly increasing accuracy over traditional methods.

Contribution

The study proposes integrating cGAN with U-Net to enhance segmentation accuracy of bitewing radiographs, achieving a 13.3% improvement over existing U-Net models.

Findings

cGAN + U-Net achieves 69.7% accuracy

Accuracy improved by 13.3% over U-Net alone

Method outperforms traditional segmentation approaches

Abstract

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%.