# Implant Model Generation Method for Mandibular Defect Based on Improved 3D Unet
## 2 Materials and Methods
### The Process of Constructing the Mandible 3D Model Dataset
#### 3.3.3 Visualization  
Table 4 shows the performance of the implants generated by the four networks described in Section 3.1 for the defects of different positions and sizes. The first and second rows represent four kinds of defects with different positions and shapes and their real implants, and the third to sixth rows represent the performance of the four kinds of network-generated implants. It can be seen from Table 4 that the implant generated by the 3 DUNet network fails to restore the shape of the real implant. The implant model occurs serious deformation and has a lot of noise. In contrast, the shape of the implant model generated by 3D RUnet and 3D DUnet is closer to the shape of the real implant, and the noise has been reduced to some extent, but it is still serious. The implant generated by 3D RDUnet has obvious improvement compared with the other three networks. Not only is the shape the closest to the real implant, but the noise is also significantly reduced. In the process of generating the implant model by the 3 DUNet network, it is difficult for the network to extract the global information of the shape and structure of the mandible, and it is also difficult to extract the shape information of the defect. As a result, when the network generates the mandible repair model, neither the overall shape of the mandible nor the shape of the defect can be restored. When performing the reconstruction-subtraction strategy to generate the implant, these errors appear as noise around the surface of the implant and at other locations away from the implant. The residual structure in the 3D RUnet network enables the network to learn the local information of the defect more specifically, resulting in the network being able to restore the shape of the defect more accurately when generating the mandible repair model. However, the learning ability of the network for the overall information of the mandible is still insufficient. In the final implant model, the shape of the implant is closer to that of the real implant, and there is less noise around the implant surface, but there is still more noise in other parts farther from the implant. In contrast, the expanded convolutional layer in the 3D DUnet network can expand the receptive field of the convolution, enabling the network to learn more about the overall shape of the mandible rather than the shape of the defect. This results in the network being able to accurately reconstruct the overall shape of the mandible but difficult to restore the shape of the defect. Therefore, in the generated implant model, noise is generated less far away from the implant and more around the surface of the implant. The residual structure and dilated convolutional layer are added to 3D RDUnet at the same time, and the generated implant is the closest to the real implant. Noise is significantly reduced, both around the implant surface and elsewhere. This shows that the residual structure and dilated convolutional layers in the network can be effective at the same time, improving the quality of the generated implants. Such a model requires only simple morphological processing to generate implants for defect repair by 3D printing, and it can meet the surgical requirements for mandibular defect repair. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Visual Comparison of Different Defect-Generating Implants**} \\ \hline \hline \multicolumn{2}{c}{Mandibular defect} \\ \hline \hline \multicolumn{2}{c}{Real implants} \\ \hline \hline \multicolumn{2}{c}{3D Unet} \\ \hline \hline \multicolumn{2}{c}{3D RUnet} \\ \hline \hline \multicolumn{2}{c}{3D RUnet} \\ \hline \hline \multicolumn{2}{c}{3D RDUnet} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Visual comparison of different defect-generating implants.