# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
## 4 Experiments and Results
### Comparison with Other Methods
We conducted paired t-test experiments to verify that the improvement in MEASegNet was statistically significant. The offline results of the above six popular models and the experimental results are shown in Table 4.  
As it can be seen in Table 4, the p-values of the Dice scores for MEASegNet and the other models were all less than 0.05, indicating that the Dice values of different models in the table are significantly different. In order to test the generalization of the proposed model, MEASegNet was trained and evaluated using two datasets, i.e., BraTS2019 and BraTS2020, under the same experimental conditions and training parameters. Since the online evaluation platform of the BraTS2019 and BraTS2020 datasets is no longer in use, the training set of the two datasets were divided into the model training set and the model validation set according to the ratio of 4:1, and the validation set results of the model were referred to as the offline results. The segmentation effect of MEASegNet on BraTS2020 and BraTS2019 was evaluated using the model validation set. MEASegNet was compared with five excellent networks, namely AMPNet, 3D U-Net, DMFNet, CANet, and AE AU-Net, in relation to the BraTS2019 dataset. Among them, 3D U-Net is a classic network, while the other four network structures are inspired to the encoder-decoder structure. Table 5 shows the comparison between MEASegNet and the other excellent networks. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**WT**} & \multicolumn{2}{c}{**TC**} & \multicolumn{2}{c}{**ET**} \\ \cline{2-7}  & **\%Subjects** & \(p\) & **\%Subjects** & \(p\) & **\%Subjects** & \(p\) \\ \hline MEASegNet (ours) vs. 3D U-Net & 78.09 & **7.427 \(\times\) 10\({}^{-15}\)** & 84.86 & **2.499 \(\times\) 10\({}^{-10}\)** & 79.68 & **0.0007** \\ MEASegNet (ours) vs. Att-Unet & 77.69 & **5.505 \(\times\) 10\({}^{-8}\)** & 84.06 & **1.087 \(\times\) 10\({}^{-8}\)** & 79.68 & **0.0032** \\ MEASegNet (ours) vs. UNETR & 77.29 & **2.481 \(\times\) 10\({}^{-7}\)** & 86.45 & **1.837 \(\times\) 10\({}^{-8}\)** & 80.48 & **0.0022** \\ MEASegNet (ours) vs. TransBTS & 82.87 & **1.046 \(\times\) 10\({}^{-7}\)** & 85.66 & **1.197 \(\times\) 10\({}^{-8}\)** & 82.87 & **5.048 \(\times\) 10\({}^{-6}\)** \\ MEASegNet (ours) vs. VT-UNet & 73.71 & 0.0052 & 82.87 & **2.370 \(\times\) 10\({}^{-7}\)** & 78.88 & **0.0048** \\ MEASegNet (ours) vs. Swin Unet3D & 71.71 & **0.0047** & 81.27 & **5.619 \(\times\) 10\({}^{-6}\)** & 78.49 & **0.0182** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ratio (in %) of the improvement in the performance of MEASegNet compared to different methods. Bold numbers indicate statistical significance (\(p<0.05\)). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Dice (\%)**} & \multicolumn{3}{c}{**HD95 (mm)**} \\ \cline{2-9}  & **WT** & **TC** & **ET** & **AVG** & **WT** & **TC** & **ET** & **AVG** \\ \hline
3D U-Net [10] & 91.29 & 89.13 & 85.78 & 88.73 & 7.50 & 5.47 & 3.82 & 5.59 \\ Att-Unet [36] & 91.43 & 89.51 & 85.71 & 88.88 & 7.30 & 5.39 & 3.81 & 5.50 \\ UNETR [37] & 91.53 & 88.57 & 85.27 & 88.46 & 7.42 & 5.98 & 3.74 & 5.71 \\ TransBTS [38] & 90.61 & 88.78 & 84.29 & 87.89 & 7.64 & 5.56 & 3.90 & 5.70 \\ VT-UNet [39] & 92.39 & 90.12 & 86.07 & 89.53 & 7.14 & 5.17 & 3.97 & 5.42 \\ Swin Unet3D [43] & 92.85 & 90.69 & 86.26 & 89.93 & 7.17 & 4.94 & 3.83 & 5.31 \\
**Our (MEASegNet)** & **93.29** & **93.16** & **88.19** & **91.55** & **6.87** & **4.57** & **3.59** & **5.01** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The offline validation results for the comparison of different methods in relation to BraTS2021, with the best performance highlighted in bold. As it can be seen from Table 5, the Dice scores of MEASegNet with respect to WT, TC, and ET of the BraTS2019 model validation set were 90.24%, 88.80%, and 80.36%, and the HD95 were 7.85, 6.10, and 4.08.