# A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-Rays
## 4 Results and Discussion
Figure 10 illustrates segmentation performance through the visualization of predictions compared to the ground truth, and it also presents the segmentation results using both horizontal bounding boxes and oriented bounding boxes. \begin{table}
\begin{tabular}{c c c} \hline \hline
**RIoU** & **FP** & **FN** \\ \hline
82.82 & 0 & 3–6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Rotated IoU (RIoU), false positives (FP), and false negatives (FN) calculated from OBBs achieved with our proposed method. Figure 10: Segmentation results: (**top**) Ground truth, (**middle**) Segmentation results with HBB, and (**bottom**) Segmentation results with OBB. Separate colors are chosen for each tooth. So far, the evaluation is based on the entire dataset. We calculate the mean evaluation matrices for each individual tooth label in the entire dataset. However, this approach sometimes does not accurately reflect the performance of individual tooth labels. Therefore, we need to evaluate the 32 individual tooth labels separately. As shown in Figure 11, we generate a radar chart to demonstrate the performance of each label. We observe that tooth labels 4, 8, 22, 24, and 32 show relatively poor performance compared to the other labels in terms of DSC and RIoU. Their DSC scores range from 80% to 85%, and their RIoUs range from 75% to 80%. In contrast, tooth labels 1-2, 9-10, 18-19, 20-21, and 27-29 demonstrate relatively high performance with DSC scores ranging from 90% to 95% and RIoU ranging from 85% to 90%. Next, we perform categorical analysis. As shown in Figure 2, 32 teeth are divided into four categories: incisors, canines, premolars, and molars. Each of these categories is further divided into two groups: upper and lower. So, in total, we have eight categories. Categorical analysis is presented in Table 4. It is observed that incisors and canines, for both upper and lower jaws, demonstrate good performance. Lower premolars also exhibit good performance. These five categories achieve a DSC of >88% and an RIoU of >83%. However, upper premolars, along with both upper and lower molars, show relatively poor performance, with a DSC of \(\leq\)86% and an RIoU of \(\leq\)80%. Figure 11: Radar diagram to demonstrate tooth-wise evaluation. We identify several factors that influence the model's performance. Lack of instance overlapping, presence of foreign bodies, fuzzy tooth roots, and poor annotations are some of the factors that affect the performance. As shown in Figure 12a,b, there are overlaps between premolar teeth and between premolar and molar teeth in the panoramic X-ray images; however, there is no instance of overlap in the ground truth. Foreign bodies include dental restorations, broken dental instruments, metal fillings, braces, and other metal objects. Figure 12c shows the presence of bracelets and fillings, while Figure 12d shows the presence of fuzzy tooth roots. As shown in Figure 12e, some images are poorly annotated and contain sharp edges and cones. However, when compared to the original data, our model predicts smoother edges, which is more reasonable. While some researchers have made modifications to improve the dataset's quality [37; 38], in this work, we have used the original dataset as it is publicly available. To better understand the impact of dataset imperfections, we manually reviewed the test set and identified several recurring issues. Specifically, at least 21 images include foreign objects (e.g., braces, caps, fillings, or screws), at least 19 images (excluding foreign objects) exhibit some form of tooth overlaps that are not reflected in the ground truth, and at least 10 images show ambiguous or fuzzy tooth roots, among which 5 also suffer from low overall image quality. Many of the overlaps are observed in category 2. These factors introduce noise and ambiguity during training and evaluation, potentially affecting the model's ability to learn clear tooth boundaries.