I. Introduction
In this study, we explore the utilization of the U-Net algorithm, a powerful convolutional neural network architecture, for the precise segmentation of teeth in panoramic X-ray images. The U-Net architecture, characterized by its encoder-decoder design with skip connections, has demonstrated remarkable performance in biomedical image segmentation tasks. The primary objective of this research is to develop an automated and accurate method for teeth segmentation, aiming to streamline the workflow of dental professionals and improve diagnostic accuracy [1]. By leveraging deep learning techniques and the U-Net architecture, we aim to create a model capable of efficiently identifying and outlining individual teeth structures within panoramic X-ray images. This investigation involves assembling a comprehensive dataset of panoramic X-ray images, each paired with meticulously annotated masks indicating the precise locations of teeth. Leveraging this dataset, the U-Net model will be trained to learn the intricate patterns and features associated with teeth structures in X-ray images. Key aspects of this research include data preprocessing techniques, model architecture design, optimization strategies, and rigorous evaluation methodologies. We will explore various metrics, such as Intersection over Union (IoU), Dice coefficient, and visual assessments, to quantify the accuracy and robustness of the proposed U-Net-based segmentation approach. The ultimate goal of this study is to develop a reliable and efficient automated teeth segmentation system that can seamlessly integrate into dental imaging workflows. Such a system has the potential to enhance the efficiency of dental professionals, improve diagnostic accuracy, and ultimately contribute to better patient care in the field of dentistry.