# Bitewing Radiography Semantic Segmentation Base on Conditional Generative Adversarial Nets

Source: [Bitewing Radiography Semantic Segmentation Base on Conditional Generative Adversarial Nets](<https://www.sota2.com/research/paper/68fa640d1e209131cea025d4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:55:10.976Z

Authors: Jiang Yun, Tan Ning, Zhang Hai, Peng Tingting

Published: 2018-02-07T05:44:48.000Z

## 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%.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1802.02571>)

[PDF](<https://arxiv.org/pdf/1802.02571.pdf>)