# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
## 4 Experiments and Results
### Comparison with Other Methods
Among them, the Dice score of AMPNet in the WT region was slightly higher than that of MEASegNet, because its model prediction part takes the minimum value at different resolutions and aggregates the information of multiscale prediction. However, MEASegNet achieved better Dice scores than AMPNet with respect to TC and ET regions. MEASegNet showed better results in terms of Dice scores compared to the remaining models in the table. Concerning HD95, AMPNet and DMFNet performed slightly better in terms of WT and ET, while MEASegNet showed a slight advantage with respect to TC. MEASegNet was also compared with four excellent networks, i.e., U-Net++, Point-UNet, TransBTS, and RFNet, with respect to the BraTS2020 dataset. Among them, Point-UNet combines U-Net with Point cloud technology, and TransBTS combines Transformer and U-Net structures. The MEASegNet results using the BraTS2020 dataset are shown in Table 6, where they are compared to the results of the other networks. As it can be seen from Table 6, the Dice scores of MEASegNet with respect to WT, TC, and ET of the BraTS2020 dataset were 91.66%, 86.97%, and 79.09%. MEASegNet showed the best performance in terms of the Dice score compared to the other models in the table. Among them, the Dice score of U-Net++ was 0.74% higher than that of MEASegNet in the ET region, because it introduces dense connections in the skip structure to fuse features of multiple scales at different levels. However, MEASegNet achieved better Dice scores than U-Net++ with respect to WT and TC regions. Given the significant impact of small targets and data class imbalance on segmentation outcomes in brain tumor MRI images, the Jaccard loss function was employed in MEASegNet. This choice was driven by its superior ability to enhance segmentation accuracy and its robust performance in handling small targets and imbalanced datasets. As illustrated in Figure 7, the network stabilized by epoch 150, yielding a smooth curve that attests to the model's rapid convergence and efficient inference capabilities. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Dice (\%)**} \\ \cline{2-5}  & **WT** & **TC** & **ET** & **AVG** \\ \hline U-Net++ [50] & 89.77 & 85.57 & **79.83** & 85.06 \\ Point-UNet [51] & 89.67 & 82.97 & 76.43 & 83.02 \\ TransBTS [38] & 90.09 & 81.73 & 78.73 & 83.52 \\ RFNet [52] & 91.11 & 85.21 & 78.00 & 84.77 \\
**Our (MEASegNet)** & **91.66** & **86.97** & 79.09 & **85.91** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The offline validation results for the comparison of different methods in relation to BraTS2020, with the best performance highlighted in bold. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Dice (\%)**} & \multicolumn{4}{c}{**HD95 (mm)**} \\ \cline{2-9}  & **WT** & **TC** & **ET** & **AVG** & **WT** & **TC** & **ET** & **AVG** \\ \hline AMPNet [46] & **90.29** & 79.32 & 75.57 & 81.73 & **4.49** & 8.19 & 4.77 & 5.82 \\
3D U-Net [10] & 88.40 & 79.60 & 77.60 & 81.87 & 9.11 & 8.68 & 4.48 & 7.42 \\ DMFNet [47] & 90.00 & 81.50 & 77.60 & 83.03 & 4.64 & 6.22 & **2.99** & **4.62** \\ CA Net [48] & 88.50 & 85.10 & 75.90 & 83.17 & 7.09 & 8.41 & 4.81 & 6.77 \\ AE AU-Net [49] & 90.20 & 81.50 & 77.30 & 83.00 & 6.15 & 7.54 & 4.65 & 6.11 \\
**Our (MEASegNet)** & 90.24 & **88.80** & **80.36** & **86.47** & 7.85 & **6.10** & 4.08 & 6.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The offline validation results for the comparison of different methods in relation to BraTS2019, with the best performance highlighted in bold. To verify the actual segmentation outcomes, three FLAIR image cases were randomly chosen from the BraTS2021 dataset for segmentation evaluation. The models used for comparison, from left to right, included 3D U-Net, Att-Unet, UNETR, TransBTS, VT-Unet, Swin UNet3D, and MEASegNet (ours), with the manually annotated ground-truth segmentation on the right.