# Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images
## 3 Results  
Tables 2 and 3 display the calculated evaluation metrics between the ground truths and the model outputs for the in-house and external tests. In the in-house test with the CenterA dataset, although the scores of HPMR-U-Net were not the best among the models, the score differences were lower compared to those for the other datasets. From the result, it can be inferred that the performance of HPMR-U-Net for the CenterA dataset was comparable to that of the other models. In the external tests, the scores of HPMR-U-Net ranked first for all external datasets. The results indicate that HPMR-U-Net has the highest performance among the models in this study for the external datasets. Comparing results among external datasets, the differences in scores were the largest in the CenterB dataset, where CenterB dataset may have the largest characteristic difference in the image obtained by CBCT as compared to CenterA dataset acquired by MDCT. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{}} & \multicolumn{4}{c}{**In-House Test**} & \multicolumn{6}{c}{**External Test**} \\ \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**CenterA**} & \multicolumn{4}{c}{**PDDCA**} & \multicolumn{4}{c}{**TCIA**} & \multicolumn{4}{c}{**CenterB**} \\ \hline \multirow{3}{*}{U-Net} & **DC** & **95 HD** & **ASD** & **DC** & **95 HD** & **ASD** & **DC** & **95 HD** & **ASD** & **DC** & **95 HD** & **ASD** \\  & **[\%]** & **[mm]** & **[mm]** & **[\%]** & **[mm]** & **[mm]** & **[\%]** & **[mm]** & **[mm]** & **[mm]** & **[\%]** & **[mm]** & **[mm]** \\ \hline \multirow{3}{*}{U-Net} & 98.3 & 0.4 & **0.0** & 63.4 & 7.3 & 1.8 & 62.8 & 9.6 & 3.2 & 61.2 & 33.7 & 4.1 \\  & \(\pm\) 0.4 & \(\pm\) 0.1 & \(\pm\) **0.0** & \(\pm\) 20.2 & \(\pm\) 5.8 & \(\pm\) 3.5 & \(\pm\) 26.3 & \(\pm\) 11.8 & \(\pm\) 7.9 & \(\pm\) 17.9 & \(\pm\) 26.1 & \(\pm\) 4.0 \\ \hline \multirow{3}{*}{\begin{tabular}{c} Res-U-Net \\ \end{tabular} } & 98.2 & 0.4 & 0.1 & 51.3 & 13.5 & 2.0 & 46.3 & 18.0 & 6.5 & 48.5 & 28.8 & 4.1 \\  & \(\pm\) 0.4 & \(\pm\) 0.1 & \(\pm\) 0.0 & \(\pm\) 20.1 & \(\pm\) 12.3 & \(\pm\) 1.8 & \(\pm\) 25.5 & \(\pm\) 19.8 & \(\pm\) 13.5 & \(\pm\) 13.1 & \(\pm\) 20.6 & \(\pm\) 3.3 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} mV- \\ Net \\ \end{tabular} } & **98.4** & **0.4** & **0.0** & 72.3 & 5.6 & 1.5 & 71.4 & 8.4 & 2.5 & 63.6 & 22.5 & 2.6 \\  & \(\pm\) **0.3** & \(\pm\) **0.0** & \(\pm\) **2.0** & \(\pm\) 21.6 & \(\pm\) 6.4 & \(\pm\) 3.5 & \(\pm\) 27.8 & \(\pm\) 12.9 & \(\pm\) 5.0 & \(\pm\) 14.7 & \(\pm\) 18.9 & \(\pm\) 2.2 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} HPMR- \\ U-Net \\ \end{tabular} } & 97.4 & 0.4 & 0.1 & **86.5** & **1.8** & **0.2** & **86.4** & **2.8** & **0.3** & **77.7** & **3.4** & **0.7** \\  & \(\pm\) 0.4 & \(\pm\) 0.1 & \(\pm\) 0.0 & \(\pm\) **3.9** & \(\pm\) **1.3** & \(\pm\) **0.1** & \(\pm\) **6.2** & \(\pm\) **7.7** & \(\pm\) **0.7** & \(\pm\) **4.1** & \(\pm\) **0.6** & \(\pm\) **0.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of in-house and external tests for mandible segmentation. The best case is bolded. Figure 5: Sample case in the CenterA dataset. White and blue indicate the mandible and maxilla, respectively. Figure 6: Sample case in the PDPCA dataset. White and blue indicate the mandible and maxilla, respectively. PDPCA has no maxilla ground truth.