# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
## 4 Experiments and Results
### Comparison with Other Methods  
To evaluate the performance of the MEASegNet model, we compared it with 11 popular models. Among them, two models were published in 2024 (Yaru3 DFPN and QT-UNet-B), two in 2023 (Swin Unet3D and SDS-Net), and two in 2022 (AABTS-Net and 3D PSwinBTS); the other five are classic models (3D U-Net, Att-Unet, UNETR, TransBTS, and VT-UNet). Among these models, those combining U-Net with attention mechanisms are Att-Unet, AABTS-Net, and SDS-Net, while the Transformer-based models are UNETR, TransBTS, 3D PSwinBTS, Swin Unet3D, and QT-UNet-B. The results of MEASegNet and the 11 networks on the BraTS2021 dataset online validation are shown in Table 2 and Figure 5.  
Figure 5: Comparison of the Dice results of different segmentation methods. The online validation results of the MEASegNet model for WT, TC, ET, and average Dice were 92.5%, 87.49%, 84.16%, and 88.05%, respectively. As shown in Figure 6, the HD95 results were 4.18, 7.96, 14.40, and 8.85, respectively. The 3D PSwinBTS model outperformed MEASegNet in relation to WT due to its advantages in modeling global or long-range contextual interactions and spatial dependencies. However, MEASegNet achieved higher Dice values with respect to TC and ET compared to 3D PSwinBTS thanks to the attention mechanisms incorporated at various stages of feature acquisition and processing. Additionally, MEASegNet exhibited slightly higher Dice values than the other networks listed in Table 2. From the HD95 perspective, 3D PSwinBTS and SDS-Net performed slightly better than MEASegNet, but the differences were not significant. Among the compared networks, MEASegNet had relatively lower HD95 values with respect to TC and ET, also indicating that the segmentation results were within an acceptable range. In order to further verify the segmentation ability of MEASegNet, the offline experimental results of six popular models, i.e., 3D U-Net, Att-UNet, UNETR, TransBTS, VT-UNet, and Swin Unet3D, were obtained through model training in the same experimental envi \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{2-10}  & **WT** & **TC** & **ET** & **AVG** & **WT** & **TC** & **ET** & **AVG** \\ \hline
3D U-Net (2016) [10] & 88.02 & 76.17 & 76.20 & 80.13 & 9.97 & 21.57 & 25.48 & 19.00 \\ Att-Unet (2018) [36] & 89.74 & 81.59 & 79.60 & 83.64 & 8.09 & 14.68 & 19.37 & 14.05 \\ UNETR (2021) [37] & 90.89 & 83.73 & 80.93 & 85.18 & 4.71 & 13.38 & 21.39 & 13.16 \\ TransBTS (2021) [38] & 90.45 & 83.49 & 81.17 & 85.03 & 6.77 & 10.14 & 18.94 & 11.95 \\ VT-UNet (2022) [39] & 91.66 & 84.41 & 80.75 & 85.60 & 4.11 & 13.20 & 15.08 & 10.80 \\
3D PSwinBTS (2022) [40] & **92.64** & 86.72 & 82.62 & 87.32 & **3.73** & 11.08 & 17.53 & 10.78 \\ AABTS-Net (2022) [41] & 92.20 & 86.10 & 83.00 & 87.10 & 4.00 & 11.18 & 17.73 & 10.97 \\ SDS-Net (2023) [42] & 91.80 & 86.80 & 82.50 & 87.00 & 21.07 & 11.99 & **13.13** & 15.40 \\ Swin Unet3D (2023) [43] & 90.50 & 86.60 & 83.40 & 86.83 & - & - & - & - \\ QT-UNet-B (2024) [44] & 91.24 & 83.20 & 79.99 & 84.81 & 4.44 & 12.95 & 17.19 & 11.53 \\ YaruSDFPN (2024) [45] & 92.02 & 86.27 & 80.90 & 86.40 & 4.09 & 8.43 & 21.91 & 11.48 \\
**Our(MEASegNet)** & 92.50 & **87.49** & **84.16** & **88.05** & 4.18 & **7.96** & 14.40 & **8.85** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The online validation results for the comparison of different methods in relation to BraTS2021, with the best performance highlighted in bold. Figure 6: Comparison of the HD95 results of different segmentation methods. ronment. Table 3 shows the offline experimental results of MEASegNet and six popular models. As it can be seen in Table 3, MEASegNet achieved a outperformance in terms of the Dice score and HD95. In order to verify that the difference in Dice scores of MEASegNet was not generated by random variation but by the actual model, performance was improved.