# A Multi Brain Tumor Region Segmentation Model Based on 3D U-Net
## 4 Experiments and Results
### Results and Discussion  
All experiments in this paper were conducted in the same training environment configuration: AMD 5950X, CPU 64 GB RAM, and NVIDIA GeForce RTX3090 24G GPU. PyTorch1.12 framework and Anaconda Python3.7 interpreter built the network framework. The weight decay is \(1\times 10^{-4}\); the total epoch is 100 times; the initial learning rate is \(3\times 10^{-4}\); the batch size is 4; and the activation function is Softmax. To evaluate the segmentation performance of the proposed model, comparative experiments were conducted in three aspects. Table 3 gives the comparison of the dice similarity coefficient of the proposed model with the other ten models in the extraction of WT, TC, and ET. From Table 3, it can be concluded that our model significantly improved overall accuracy compared to the 2D segmentation networks, especially in the ET area. It also handles the details of the TC area well. The dice similarity coefficient of WT reaches 0.778; TC dice reaches 0.875, which is about 0.054 higher than that of the 2D U-Net; ET dice reaches 0.903, which is about 0.137 higher than that of the 2D U-Net. Table 4 gives the sensitivity comparison of the proposed model with the other ten models in the extraction of WT, TC, and ET. As shown in Table 4, the sensitivity of WT, TC, and ET reaches 0.906, 0.926, and 0.946, respectively, which represents an improvement of 3.8%, 0.9%, and 6.5% compared to the 2D U-Net. This shows that the 3D U-Net model designed in this paper can better handle the segmentation of different tumor regions. Table 5 gives the precision comparison of the proposed model with the other ten models in the extraction of WT, TC, and ET. As shown in Table 5, the model in this paper \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **WT Dice** & **TC Dice** & **ET Dice** \\ \hline U-Net & 0.838 & 0.821 & 0.768 \\ U-Net++ & 0.842 & 0.826 & 0.774 \\ PA-Net & 0.801 & 0.681 & 0.718 \\ S3D-UNet [19] & 0.880 & 0.756 & 0.659 \\ VAE-encoder [20] & 0.882 & 0.814 & 0.733 \\ DANet [29] & 0.842 & 0.853 & 0.797 \\ ResUnet [30] & 0.843 & 0.861 & 0.787 \\ AttUNet [31] & 0.849 & 0.866 & 0.789 \\ MA-UNet [32] & 0.738 & 0.743 & 0.577 \\ SAU-Net [33] & 0.815 & 0.843 & 0.712 \\ ANU-Net [34] & 0.811 & 0.866 & 0.733 \\ Attention-UNet [35] & 0.843 & 0.858 & 0.685 \\ Ours & 0.778 & 0.875 & 0.903 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of dice similarity coefficient with ten other models. outperforms other models in terms of TC precision and ET precision, reaching 0.895 and 0.946, respectively. However, WT precision is relatively low, only reaching 0.801. The binary cross-entropy loss function can optimize the network in the process of network training, effectively solve the problem of vanishing gradient in the network, and make the network run stably. However, when faced with images with class imbalance problems, the loss function will exhibit bias toward samples with many classes, thus affecting the optimization direction of the network. The dice similar loss function can guide the learning of network parameters and make the prediction result close to the real value. In this paper, experiments are conducted to verify whether adding the dice similar loss function to the binary cross-entropy loss function can improve the performance of glioma segmentation. The experimental results are shown in Table 6.  
It can be seen from Table 5 that the mixed loss function composed of binary cross- entropy loss function and dice similarity coefficient can alleviate the class imbalance problem in brain glioma image segmentation to a certain extent and obtain good network segmentation results.