# Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**In-House Test**} & \multicolumn{3}{c}{**External Test**} \\ \hline \hline  & \multicolumn{3}{c}{**CenterA**} & \multicolumn{3}{c}{**CenterB**} \\ \hline  & \multicolumn{1}{c}{**DC**} & **95 HD** & **ASD [mm]** & **DC** & **95 HD** & **ASD [mm]** \\  & **[\%]** & **[mm]** & **ASD [mm]** & **[\%]** & **[mm]** & **ASD [mm]** \\ \hline \multirow{2}{*}{U-Net} & **96.5** & 0.4 & 0.1 & 75.0 & 9.0 & 1.1 \\  & **\(\pm\) 0.8** & \(\pm\) 0.1 & \(\pm\) 0.0 & \(\pm\) 5.7 & \(\pm\) 8.8 & \(\pm\) 0.8 \\ \hline \multirow{2}{*}{Res-U-Net} & 96.2 & 0.4 & 0.1 & 67.6 & 17.1 & 2.5 \\  & \(\pm\) 0.8 & \(\pm\) 0.1 & \(\pm\) 0.0 & \(\pm\) 12.3 & \(\pm\) 18.1 & \(\pm\) 3.4 \\ \hline \multirow{2}{*}{mU-Net} & 96.5 & **0.4** & 0.1 & 75.9 & 8.6 & 1.0 \\  & \(\pm\) 0.7 & \(\pm\) **0.0** & \(\pm\) 0.0 & \(\pm\) 5.1 & \(\pm\) 7.9 & \(\pm\) 0.7 \\ \hline HPMR-U- & 90.2 & 0.5 & 0.1 & **82.8** & **2.7** & **0.4** \\ Net & \(\pm\) 19.5 & \(\pm\) 0.1 & \(\pm\) 0.0 & \(\pm\) **3.2** & \(\pm\) **1.6** & \(\pm\) **0.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of in-house and external tests for maxilla segmentation. The best case is bolded. There were prominent decreases in quality of segmentations from other models for the external datasets. For the PDDCA dataset in Figure 6, the DC scores for the mandible of U-Net and mU-Net were 0.0%, as the model could not find the mandible at all, that is, they were unable to distinguish between the mandible and the maxilla. There were also many losses in the segmentations of the mandible and maxilla in the outputs of Res-U-Net. By contrast, the outputs of HPMR-U-Net were more intact and closer to the ground truth. As the teeth were included in the CenterA dataset segmentations that were used in training, the teeth were also segmented, despite not being in the ground truth. The results for the TCIA dataset in Figure 7 are also similar to those of the PDDCA dataset. U-Net and Res-U-Net failed to segment the mandible, which resulted in a 0.0% DC. Additionally, mU-Net included many portions of the maxilla in the mandible output and lost a large portion of the segmentations. However, HPMR-U-Net exhibited high performance with a DC of 91.7%. As displayed in Figure 8 for the CenterB dataset, HPMR-U-Net also showed the highest performance among the models, with many lost sections in the other models. Furthermore, the other models were more unable to accurately separate the mandible and maxilla compared to HPMR-U-Net. Figure 8: Sample case in CenterB dataset. White and blue indicate the mandible and maxilla, respectively. Figure 7: Sample case in the TCIA dataset. White and blue indicate mandible and maxilla, respectively. TCIA has no maxilla ground truth. Figure 9 shows rendered color maps in 3D for the distance from the ground truths to the output segmentations of the best DC case for the mandible in CenterA dataset to thoroughly examine the differences among the model outputs for this dataset. There were no significant differences, but the distances in the mandibular foramen were slightly different. This part is challenging to segment accurately owing to its small size, and the distance was less in the outputs of HPMR-U-Net than the other models.