PanoGAN A Deep Generative Model for Panoramic Dental Radiographs

2025-07-28

AI Summary

To address the scarcity of panoramic dental radiograph data in dental research and education, this paper proposes a deep convolutional generative adversarial network (DCGAN) based on Wasserstein GAN with gradient penalty (WGAN-GP), specifically designed for anatomically plausible synthesis of alveolar region imagery. We systematically investigate the impact of discriminator architecture, feature depth, and denoising preprocessing on generation quality, revealing an inherent trade-off between fine-detail fidelity and global structural clarity. Leveraging large-scale data cleaning and standardization, the synthesized images achieve moderate anatomical expressiveness as assessed by clinical experts, striking a favorable balance between structural detail preservation and image sharpness. Although minor artifacts persist, quantitative and qualitative evaluations confirm the feasibility and practical potential of GANs for generating diagnostic-quality dental radiographs.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Addressing scarcity of dental panoramic radiograph data
• Developing GAN for synthesizing dental radiographs
• Evaluating model performance on anatomical visibility and realism

Innovation

• DCGAN with Wasserstein loss and gradient penalty
• Extensive preprocessing to standardize inputs
• Trade-off between detail and clarity in denoising