PanoGAN A Deep Generative Model for Panoramic Dental Radiographs

TL;DR

This study develops a GAN model to synthesize dental panoramic radiographs, aiming to mitigate data scarcity in dental research and education, with promising results in image realism and anatomical detail.

Contribution

Introduces a deep convolutional GAN trained on dental radiographs with innovative preprocessing and model variations, advancing synthetic dental image generation.

Findings

Most generated images had moderate anatomical visibility.

Denoised training data improved overall image clarity.

Non-denoised data captured finer anatomical details.

Abstract

This paper presents the development of a generative adversarial network (GAN) for synthesizing dental panoramic radiographs. Although exploratory in nature, the study aims to address the scarcity of data in dental research and education. We trained a deep convolutional GAN (DCGAN) using a Wasserstein loss with gradient penalty (WGANGP) on a dataset of 2322 radiographs of varying quality. The focus was on the dentoalveolar regions, other anatomical structures were cropped out. Extensive preprocessing and data cleaning were performed to standardize the inputs while preserving anatomical variability. We explored four candidate models by varying critic iterations, feature depth, and the use of denoising prior to training. A clinical expert evaluated the generated radiographs based on anatomical visibility and realism, using a 5-point scale (1 very poor 5 excellent). Most images showed moderate anatomical depiction, although some were degraded by artifacts. A trade-off was observed the model trained on non-denoised data yielded finer details especially in structures like the mandibular canal and trabecular bone, while a model trained on denoised data offered superior overall image clarity and sharpness. These findings provide a foundation for future work on GAN-based methods in dental imaging.