# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 4 Experiments
### Visualization of the Segmentation Results  
#### 4.3.1 Results on the Meningioma Dataset  
In this section, a comparison of the predictions of the ECA model with other models on the meningioma dataset is shown in Figure 4. By comparing the segmentation of each model, it can be seen that there are significant differences in the accuracy and boundary clarity of the different models in meningioma segmentation. UNETR's predictions are unclear at the boundaries, and its segmentation areas are small, failing to segment the whole tumor. In addition, there is an obvious error in SwinUNETR, nnFormer, nnUNet, and UNETR++, that is, these models identify regions that are non-tumors as tumors. Compared with other models, the segmentation by ECA is the closest to the ground truth, especially in the meningioma regions with complex details, indicating that the feature extraction and boundary processing capabilities of ECA are superior.  
To verify the consistency and bias of ECA in segmenting 3D medical images, bland-altman analysis is used to compare the performance of ECA with other models. The results as shown in Figure 5, where the horizontal axis of the figure represents the mean value of the segmentation and the vertical axis represents the difference between the actual value and the predicted difference. The closer the difference value is to zero, the higher the consistency of the segmentation. UNETR and SwinUNETR have more fluctuating difference values, and a more dispersed distribution up and down, indicating that they have poor consistency in segmentation. nnFormer and UNETR++ have a relatively small deviation, but still have some data points that are far away from the zero line. ECA possesses the smallest difference values (69.17) and data fluctuations, indicating that the ECA method has the best consistency in segmentation, further validating its superiority in segmentation tasks.  
In addition, in order to test the segmentation accuracy of ECA in a noisy environment, we added Gaussian noise to the test set of the meningioma dataset and re-examined the segmentation, as shown in Table 4, and found that, after the addition of noise, the value  
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline \multirow{3}{*}{**Model**} & \multicolumn{3}{c}{**DSC (\%)**} & \multicolumn{3}{c}{**HD95 (mm)**} & \multirow{3}{*}{**FLOPs**} & \multirow{3}{*}{**Params**} \\ \cline{3-3} \cline{5-10}  & & **WT** & & & & & **WT** & & **TC** & **ET** \\ \hline \multirow{8}{*}{M} & UNETR & 79.88 & 84.82 & 83.70 & 4.98 & 4.25 & 3.01 & - & - \\  & SwinUNETR & 79.94 & 86.06 & 85.43 & 4.24 & 4.44 & 2.71 & - & - \\  & nnFormer & 80.94 & 86.25 & 85.64 & 3.96 & 4.31 & 2.67 & - & - \\  & UNETR++ & 80.67 & 85.86 & 84.59 & 4.71 & **2.99** & 4.10 & - & - \\  & dResU-Net & 81.99 & 86.97 & 86.32 & 5.02 & 4.41 & 5.65 & - & - \\  & CU-Net & 79.23 & 85.16 & 84.04 & 5.67 & 6.37 & 5.49 & - & - \\  & nnUNet & 81.47 & 86.81 & 85.83 & 4.01 & 3.43 & 3.02 & - & - \\ \hline \multirow{8}{*}{A} & 3D CBAM & 78.31 & 84.51 & 83.82 & 5.25 & 5.19 & 4.02 & 981.96 & 22.48 \\  & 3D SA & 80.95 & 86.36 & 84.28 & 4.01 & 3.67 & 2.92 & **977.22** & **22.41** \\ \cline{1-1}  & HDC & 81.91 & 86.33 & 86.12 & 3.83 & 4.35 & 2.52 & 1283.00 & 26.87 \\ \cline{1-1}  & TDPC & 79.78 & 84.99 & 84.81 & 4.30 & 4.32 & 3.10 & 1280.00 & 26.86 \\ \cline{1-1} \cline{2-10}  & ECA & **83.07** & **87.04** & **87.15** & **3.69** & 3.73 & **2.26** & 979.01 & 23.02 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons with the SOTA methods and other attention mechanisms on the BraTS2021 test set. M represents ’method’ and A represents ’attention’. The bold is the best result.  
of the DSC only decreased by 0.2%, and the difference of the HD95 was 0.12%, which fully demonstrated its superior ability in eliminating noise. Also a visualization of the segmentation after adding noise as shown in Figure 6 shows the effect of our segmentation.  
Figure 4: Visualization results of models on the meningioma dataset.  
Figure 5: _Cont_.