A Robust Generative Segmentation Method for Panoramic Dental Radiography Images

Panoramic imaging is commonly used by dentists in both routine practice and in the planning of dental treatments. The process of capturing dental panoramic images presents a challenge in the segmentation of tooth components and the identification of features that form the basis of treatment planning. This is due to a number of factors, including the generation of different noise levels by the machine, the low contrast of edges, and the overlapping of anatomical structures. Furthermore, the segmentation of panoramic images presents a challenge in that a robust method is required which is capable of segmenting all tooth components in a variety of scenarios, including the presence of fillings, braces, implants, prosthetic dental crowns, and missing teeth. To address these issues, this study proposes the use of a generative model for the segmentation of panoramic dental images. The proposed Generative Adversarial Networks (GAN) model is trained to learn the spatial information between the original panoramic radiography images and the ground truth images, which contain the boundaries of the image components. Our model is evaluated on the UESB dataset, and its segmentation performance is compared with that of the U-Net model and SOTA methods evaluated on the UESB dataset. The GAN model achieved segmentation results of 0.8715 Jaccard, 0.9304 Dice, 0.9353 Precision, and 0.9293 Recall without the need for pre- or post-processing. The model demonstrated superior performance to the U-Net model and exhibited a level of performance that could compete with other convolutional neural network models. The segmentation performance of the model was validated through the conduct of an ablation study based on loss functions. The findings of the quantitative and qualitative analysis substantiate that our model is both robust and has superior performance in terms of segmentation. Furthermore, these findings exemplify the potential of GAN models as an effective methodology for computer-aided tooth segmentation, diagnosis, and treatment planning.