## 5 Discussion  
### Quantitative Analysis of Liver Segmentation  
To verify the effectiveness of MDAU-Net, we tested the method on the LiTS and SLiver07 datasets and compared it with other widely used segmentation methods. Quantitative Analysis of Liver Segmentation on LiTS. The results of the liver segmentation on the LiTS dataset are shown in Table 4. The Dice, Precision, Recall, VOE, and RVD of MDAU-Net were 0.9433, 0.9515, 0.9451, 0.1053, and 0.0383, which were increased by 0.0952, 0.0636, 0.0706, 0.1483, and 0.2159, respectively, compared with the baseline U-Net values. Meanwhile, MDAU-Net had a significantly better balance between accuracy and recall, and the performance was outstanding in liver organ segmentation when compared to previous networks. Figure 9a shows the radar plots of the quantitative analysis of different models using LiTS. The red line represents MDAU-Net, which has the largest area covered by metrics on the axes, so that it can be more intuitively observed that its performance was better than the other comparison models. Figure 10 displays the visualized results of the liver segmentation comparison test in this section. In these figures, the green lines represent the actual labels of the CT images, while the red lines indicate the prediction results. Additionally, we zoomed \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Dice** & **Precision** & **Recall** & **VOE** & **RVD** \\ \hline U-Net & 0.8481 & 0.8879 & 0.8745 & 0.2536 & 0.2698 \\ RU-Net [27] & 0.8614 & 0.8902 & 0.8807 & 0.2415 & 0.2501 \\ ResUNet [28] & 0.9220 & 0.9263 & 0.9450 & 0.1427 & 0.0599 \\ Attention U-net [29] & 0.9197 & 0.9189 & 0.9236 & 0.1463 & 0.0575 \\ UNet++ [10] & 0.9106 & 0.9173 & 0.9075 & 0.1591 & 0.0818 \\ SAR-U-Net [30] & 0.9378 & 0.9504 & 0.9326 & 0.1142 & 0.0736 \\ ResBCU-Net [31] & 0.9359 & 0.9428 & 0.9302 & 0.1810 & 0.0587 \\ RMS-UNet [32] & 0.9171 & 0.9227 & 0.9157 & 0.1492 & 0.0646 \\ MD-UNET [33] & 0.9338 & 0.9433 & 0.9331 & 0.1224 & 0.0604 \\ MDAU-Net (our model) & **0.9433** & **0.9515** & **0.9451** & **0.1053** & **0.0383** \\ \hline \hline \end{tabular} Bold text in the table represents the optimal results.  
\end{table} 
Table 4: Liver semantic segmentation results of different models on LiTS. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Dice** & **Precision** & **Recall** & **VOE** & **RVD** \\ \hline Baseline & 0.8481 & 0.8879 & 0.8745 & 0.2536 & 0.2698 \\ Baseline + DLE & 0.9204 & 0.9424 & 0.9408 & 0.1262 & 0.0446 \\ Baseline + ResBlock & 0.9375 & 0.9409 & 0.9437 & 0.1161 & **0.0293** \\ Baseline + CAE & **0.9447** & 0.9422 & 0.9445 & 0.1064 & 0.0339 \\ Baseline + DLE + CAE & 0.9371 & 0.9437 & 0.9436 & 0.1062 & 0.0412 \\ Baseline + DAE + ResBlock & 0.9407 & 0.9425 & 0.9431 & 0.1056 & 0.0395 \\ Baseline + ResBlock + CAE & 0.9419 & 0.9354 & 0.9443 & 0.1070 & 0.0407 \\ MDAU-Net & 0.9433 & **0.9515** & **0.9451** & **0.1053** & 0.0383 \\ \hline \hline \end{tabular} Bold text in the table represents the optimal results.  
\end{table} 
Table 3: The performance of ablation experiments on LiTS. in on specific areas for easier observation. It can be inferred that due to the relatively simple structure of the jump connection between ResUNet and SAR-U-Net codecs, there was ineffective fusion of deep and shallow features, resulting in less precise image detail processing and a noticeable loss of edge information. Moreover, U-Net and UNet++ exhibited a limited utilization of a priori knowledge, such as shallow features, leading to difficulties in distinguishing between similar tissues and more prominent instances of mis-segmentation, where background organs were mistakenly segmented as the liver. In contrast, the visual segmentation results of MDAU-Net displayed the most complete segmentation and label curves, effectively fitting both continuous and truncated regions, with no significant instances of mis-segmentation or over-segmentation in detail processing.