# A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-Rays
## 4 Results and Discussion  
We can divide our experiments into two parts: segmentation analysis and OBB analysis. In segmentation analysis, we compare our proposed model with state-of-the-art segmentation models. Since our goal is to develop an encoder-decoder-based architecture, we primarily compare the results with popular encoder-decoder structures. We tabulate the results in Table 2. As shown in the table, our proposed model achieves an IoU score of 82.43% and a DSC score of 90.37%, both of which are the highest scores compared to the other models. Incorporating grid-based attention gates led to a -1.5% IoU improvement over the original FUSegNet. The post-processing described in Section 3.3 improves the IoU from 82.0 to 82.43 and the DSC from 90.1 to 90.37. While the gain may appear modest, this step helps to remove artifacts and ensures cleaner segmentation masks, particularly around tooth boundaries. We also explore two transformer-based approaches. The first one is the Mixed Vision Transformer (MiT)-b2-UNet, for which we incorporate the SegFormer (b2-sized) encoder [27] pretrained on ImageNet with the UNet architecture. The second one is Swin-Unet [28]. However, we do not observe satisfactory performance with transformer-based approaches. Possibly, it is because transformers often tend to overfit for small-scale data due to the lack of structural bias [29] or because they require additional task-specific tuning [30]. Figure 7 provides a graphical summary of the IoU, Precision, Recall, and DSC scores reported in Table 2, illustrating the overall performance of each model, with our proposed method achieving the highest scores across all metrics. Figure 7: Bar chart comparison of segmentation performance across different deep-learning models. The proposed model achieves the highest scores in almost all evaluation metrics, indicating its superior performance. Figure 8 illustrates the training and validation performance of the proposed model over 50 epochs. The model shows stable convergence with steadily decreasing loss and consistently high IoU and DSC scores on the validation set. For OBB performance analysis, we first generate oriented bounding boxes using the method described earlier and calculate rotated IoU (RIoU). We prefer IoU over mean average precision because, unlike other region proposal-based detection methods, oriented bounding boxes generated from encoder-decoder-based approaches do not have any confidence scores. Figure 9 illustrates the \(PCA_{angle}\) for several example teeth by overlaying the first principal component on each tooth shape. This angle, measured relative to the horizontal axis, is then used to compute the tooth rotation angle, \(\theta\), for generating OBBs. As shown in Table 3, we achieve an RIoU score of 82.82%. \begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **IoU** & **Precision** & **Recall** & **DSC** \\ \hline DeepLabV3+ [(31)] & 80.45 & 88.59 & 89.75 & 89.17 \\ FPN [(32)] & 81.75 & 89.54 & 90.28 & 89.76 \\ MANet [(33)] & 62.70 & 85.82 & 69.95 & 77.08 \\ LinkNet [(34)] & 35.25 & 73.63 & 40.35 & 52.13 \\ FUSegNet [(4)] & 80.79 & 88.51 & 90.26 & 89.38 \\ PSPNet [(35)] & 28.94 & 59.60 & 36.00 & 44.89 \\ UNet [(36)] & 67.93 & 86.00 & 76.37 & 80.90 \\ MiT-b2-UNet [(27)] & 72.65 & 86.80 & 81.67 & 84.16 \\ Swin-Unet [(28)] & 53.69 & 72.43 & 67.47 & 69.87 \\ Ours & 82.43 & 90.48 & 90.26 & 90.37 \\ \hline \end{tabular}
\end{table} 
Table 2: Segmentation results achieved from deep-learning models. Figure 8: Training and validation performance of the proposed model over 50 epochs: (**left**) loss curve, (**middle**) Intersection-over-Union (IoU) curve, and (**right**) Dice Similarity Coefficient (DSC) curve. Figure 9: Visualization of \(\text{PCA}_{\text{Angle}}\) for selected teeth. The first principal component (PC1) is overlaid on the tooth shape, illustrating its orientation with respect to the horizontal axis. This angle is used to calculate the tooth’s rotation angle (\(\theta\)) for generating oriented bounding boxes (OBB).