## 4 Discussion  
Accurate segmentation of teeth from OPG images is crucial for various dental applications, such as orthodontics, prosthodontics, and dental implant planning, but also for bone age estimation [30] and forensic dentistry [31]. Manual segmentation of teeth is time-consuming and prone to subjective errors. Therefore, there is a growing interest in developing automatic segmentation methods to streamline the process and improve accuracy, even if automatic segmentation could also be limited by different factors, such as image quality, occlusions, and overlapping structures. We proposed a CNN trained on images collected from one public database (Tufts), divided into 760, 190, and 50 images for the training, validation, and test sets, respectively. Our model achieved high detection accuracy on the test set (98.4%) with a Dice index of 0.87. Through deepening the performance of individual teeth, it can be noted that some classes do not achieve 100% accuracy. Among the various factors that can cause this lowering of performance, panoramic RXs, in which many teeth are missing, have been identified as particularly challenging cases. Due to the absence of teeth that are always present in the most common cases, the model probably has fewer references for the correct identification of the single tooth. It is more common in this situation that the few teeth that are present are misclassified, the lower performance is, therefore, for the majority, due to these particular and more challenging cases. The lower performance of these cases is also reflected in the calculation of the Dice scores; the presence of two outliers (Figure 4) in fact can be attributed to the same issue. Limited experiences are reported in the literature on this topic. Different authors applied different approaches mainly based on CNNs, reaching high levels of accuracy in teeth segmentation, as in our case. [PERSON] et al. [32] focused on the development of a semantic segmentation method to accurately delineate the maxillary teeth and palatal rugae in two-dimensional dental images, as they serve as unique anatomical features for dental identification and analysis. The authors used a dataset of 797 photographic images from the Division of Orthodontics and Dentofacial Orthopedics at the American University of Beirut Medical Center, applying their analysis to colored two-dimensional (2D) images, instead of X-ray images, and studying the maxillary teeth and palate belonging to various malocclusions. Due to this different image collection approach, their results are not comparable to ours. Figure 6: Distribution of the number of false negatives for the 50 patients of the test set. The authors utilized a CNN architecture known as U-Net, which has demonstrated excellent performance in image segmentation tasks. The network was trained using a large dataset of annotated two-dimensional dental images, with each image labelled for the maxillary teeth and palatal rugae. Semantic segmentation aims to assign a specific class label to each pixel in an image, allowing for the precise localization and segmentation of different objects or regions. In this study, the semantic segmentation network was trained to distinguish between maxillary teeth and palatal rugae. By leveraging the unique features and patterns of these structures, the network learned to accurately segment them from dental images. The performance of the proposed semantic segmentation method was evaluated using various metrics, including accuracy, precision, recall, and IoU. The results demonstrated high accuracy and effectiveness in segmenting both maxillary teeth and palatal rugae. The trained network achieved a high IoU score, indicating a significant overlap between the automatic segmentation results and the manual annotations. The successful semantic segmentation of maxillary teeth and palatal rugae in two-dimensional dental images has significant clinical implications. The accurate delineation of these structures can aid in dental identification, forensic analysis, and treatment planning.