Semantic Segmentation on Panoramic Dental X-Ray Images Using U-Net Architectures

Abstract

The field of medical image analysis is in a constant state of evolution, particularly in the challenging tasks of segmenting organs, diseases, and abnormalities. Therefore, in the realm of dental disease diagnosis, image segmentation plays a crucial role in addressing the difficulties faced by dentists worldwide when diagnosing dental diseases with the naked eye. One prominent deep neural network architecture, known as U-Net, originally designed for biomedical image segmentation, has seen multiple variations and advancements aimed at improving its performance. However, the lack of comparative studies has made it challenging to assess the effectiveness of these U-Net variants in segmenting dental X-ray images. The primary objective of this research is to conduct a comprehensive performance comparison among various U-Net architectures for dental image segmentation. Specifically, we examine six U-Net architecture variants: Vanilla U-Net, Dense U-Net, Attention U-Net, SE U-Net, Residual U-Net, and R2 U-Net. These variants employ configurations with two and three convolutional layers in both the encoder and decoder blocks. Our evaluation metrics include Accuracy, Dice coefficient, F1 score, and IoU (Intersection over Union). Among these U-Net variants, Vanilla U-Net, with two convolutional layers, demonstrated the highest level of performance, achieving an Accuracy of 95.56% and an IoU score of 88% on the validation set. Notably, this model also exhibited a shorter processing time compared to the other architectures. Conversely, when employing three convolutional layers, the Dense U-Net variant emerged as the top performer, achieving an Accuracy of 95.94% and an IoU score of 89.07% on the validation set. Furthermore, the segmentation process successfully isolates the teeth from the surrounding structures, which holds promise for improving disease detection through the development of automated disease diagnosis models.