# A Multi Brain Tumor Region Segmentation Model Based on 3D U-Net
## 4 Experiments and Results
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model Structure**} & \multicolumn{3}{c}{**Dice**} & \multicolumn{3}{c}{**Sensitivity**} & \multicolumn{3}{c}{**Precision**} \\ \cline{2-10}  & **WT** & **TC** & **ET** & **WT** & **TC** & **ET** & **WT** & **TC** & **ET** \\ \hline Model+BCE & 0.871 & 0.819 & 0.775 & 0.891 & 0.825 & 0.789 & 0.885 & 0.892 & 0.767 \\ Model+BCE+Dice & 0.778 & 0.875 & 0.903 & 0.906 & 0.926 & 0.946 & 0.801 & 0.895 & 0.946 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Segmentation evaluation results of different loss functions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **WT Sensitivity** & **TC Sensitivity** & **ET Sensitivity** \\ \hline U-Net & 0.868 & 0.917 & 0.801 \\ U-Net++ & 0.868 & 0.912 & 0.827 \\ PA-Net & 0.800 & 0.675 & 0.759 \\ S3D-UNet [19] & 0.886 & 0.737 & 0.704 \\ VAE-encoder [20] & 0.891 & 0.815 & 0.745 \\ DANet [29] & 0.885 & 0.915 & 0.839 \\ ResUnet [30] & 0.875 & 0.895 & 0.843 \\ AttUNet [31] & 0.878 & 0.918 & 0.848 \\ MA-UNet [32] & 0.795 & 0.815 & 0.653 \\ SAU-Net [33] & 0.788 & 0.858 & 0.758 \\ ANU-Net [34] & 0.858 & 0.842 & 0.763 \\ Attention-UNet [35] & 0.852 & 0.893 & 0.808 \\ Ours & 0.906 & 0.926 & 0.946 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of sensitivity with ten other models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **WT Precision** & **TC Precision** & **ET Precision** \\ \hline U-Net & 0.856 & 0.853 & 0.774 \\ U-Net++ & 0.866 & 0.850 & 0.791 \\ PA-Net & 0.792 & 0.695 & 0.681 \\ S3D-UNet [19] & 0.891 & 0.852 & 0.635 \\ VAE-encoder [20] & 0.895 & 0.854 & 0.758 \\ DANet [29] & 0.859 & 0.893 & 0.781 \\ ResUnet [30] & 0.871 & 0.891 & 0.787 \\ AttUNet [31] & 0.888 & 0.892 & 0.786 \\ MA-UNet [32] & 0.732 & 0.799 & 0.574 \\ SAU-Net [33] & 0.755 & 0.783 & 0.678 \\ ANU-Net [34] & 0.703 & 0.795 & 0.704 \\ Attention-UNet [35] & 0.788 & 0.808 & 0.655 \\ Ours & 0.801 & 0.895 & 0.946 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of precision with ten other models. To verify the advantages of this paper's model more intuitively, the change curves of loss and lou with the number of epochs in the training and validation sets during the training process are shown in Figure 6, which can intuitively show the superiority of this paper's algorithm. The extraction effects of different tumor regions are shown in Figure 7. The first column depicts the original Flair MR images of four different cases. Columns 2-4 are their corresponding segmentation results of the GT, 2D U-Net, and 3D U-Net models in this paper. As can be seen in Figure 7a, the overall segmentation effect of the network in this paper is better, and the boundaries of different tumor regions can be segmented more accurately,  
Figure 6: Curves of loss and lou for the training and validation sets, where (**a**) is a plot of the variation of loss with epoch and (**b**) is a plot of the variation of lou with epoch. Figure 7: Segmentation results of four different cases: (**a**) Case 1; (**b**) Case 2; (**c**) Case 3; (**d**) Case 4.  
especially the tumor core area, and the segmentation results are similar to GT. As can be seen in Figure 7b,c, the tumor enhancement region and the core region are segmented more accurately, and the contour boundary is clearer, and the brain tumor segmentation results prove that the algorithm in this paper has better segmentation performance. In Figure 7d, the location of the tumor is determined, but the boundary segmentation of the edema area around the tumor is relatively rough, and the segmentation effect of the whole tumor area is not very satisfactory, and the network will continue to be improved subsequently.