## 5 Discussion
### Quantitative Analysis of Liver Tumor Segmentation  
On the LiTS dataset, we conducted a further comparison of MDAU-Net's performance in tumor segmentation tasks with other methods, and the results are presented in Table 6. When combined with the radar plot depicted in Figure 14, it is evident that MDAU-Net outperformed other methods in terms of Dice, VOE, and RVD, achieving values of 0.8387, 0.2699, and \(-\)0.0743, respectively. These values were 0.213, 0.1898, and 0.1929 higher than those obtained with UNet, indicating an overall superior segmentation performance compared to the other methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Dice** & **Precision** & **Recall** & **VOE** & **RVD** \\ \hline U-Net & 0.8568 & 0.9606 & 0.9588 & 0.1501 & 0.1619 \\ RU-Net [27] & 0.9032 & 0.9617 & 0.9546 & 0.1012 & 0.0523 \\ ResUNet [28] & 0.9697 & 0.9693 & 0.9740 & 0.0591 & 0.0184 \\ Attention U-net [29] & 0.9617 & 0.9501 & 0.9749 & 0.0733 & \(-\)0.0254 \\ UNet++ [10] & 0.9703 & 0.9696 & 0.9515 & 0.0574 & \(-\)0.0117 \\ SAR-U-Net [30] & 0.9655 & 0.9672 & 0.9746 & 0.0664 & \(-\)0.0184 \\ ResBCU-Net [31] & 0.9658 & 0.9647 & 0.9723 & 0.0610 & \(-\)0.0229 \\ RMS-UNet [32] & 0.9673 & 0.9601 & 0.9755 & 0.0591 & \(-\)0.0238 \\ MD-UNET [33] & 0.9679 & 0.9732 & 0.9746 & 0.0601 & \(-\)0.0162 \\ MDAU-Net (our model) & **0.9706** & **0.9743** & **0.9757** & **0.0569** & \(-\)**0.0095** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Liver semantic segmentation results for different models on SLiver07. Figure 12: Visualization of results of liver segmentation using different methods on SLiver07. The visualization of the tumor segmentation results is presented in Figure 15. It is apparent that UNet and UNet++ exhibited insufficient segmentation and diagnostic errors when dealing with lesions characterized by blurred boundaries and small sizes. On the other hand, ResUNet and SAR-U-Net faced challenges in distinguishing between similar tissues, leading to suboptimal segmentation results. In contrast, MDAU-Net excelled in effectively localizing lesion tissues and accurately segmenting border regions, particularly for non-contiguous and small-sized lesions, demonstrating significantly improved performance. This underscores the effectiveness of the proposed method in addressing the issue of useful information loss, reducing the semantic gap between different pathways and achieving segmentation results with clear boundaries between lesion regions and normal tissues. Consequently, the proposed method holds substantial clinical value. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Dice** & **Precision** & **Recall** & **VOE** & **RVD** \\ \hline U-Net & 0.6257 & 0.6013 & 0.6128 & 0.4597 & \(-\)0.2672 \\ RU-Net [27] & 0.6528 & 0.6233 & 0.6657 & 0.3926 & \(-\)0.2519 \\ ResUNet [28] & 0.8254 & 0.8027 & 0.8550 & 0.2874 & \(-\)0.0798 \\ Attention U-net [29] & 0.6683 & 0.6620 & 0.6807 & 0.3819 & \(-\)0.0818 \\ UNet++ [10] & 0.7397 & 0.9340 & 0.7599 & 0.3995 & \(-\)0.1930 \\ SAR-U-Net [30] & 0.8096 & **0.8317** & 0.8101 & 0.3495 & \(-\)0.0770 \\ ResBCU-Net [31] & 0.6818 & 0.6243 & 0.7935 & 0.4588 & \(-\)0.2278 \\ RMS-UNet [32] & 0.6712 & 0.6258 & 0.7829 & 0.4031 & \(-\)0.2517 \\ MD-UNET [33] & 0.7838 & 0.7289 & 0.8593 & 0.3447 & \(-\)0.1596 \\ MDAU-Net (our model) & **0.8387** & 0.8211 & **0.8736** & **0.2699** & \(\mathbf{-0.0743}\) \\ \hline \hline \end{tabular} Bold text in the table represents the optimal results.  
\end{table} 
Table 6: Tumor segmentation results of different models on **LITS**. Figure 13: Confusion matrix from liver segmentation in Section 5.1 on SLiver07. The confusion matrix illustrating the results of liver tumor segmentation on the LiTS dataset is displayed in Figure 16. Overall, all of these models demonstrated a high level of segmentation accuracy for non-diseased regions. In contrast, the segmentation results obtained by MDAU-Net were notably superior, with only a very small number of samples misclassified as non-diseased regions.