## 4 Experiments
### Ablation Experiment  
In this section, we conducted different ablation experiments to analyze the proposed model comprehensively. First, we evaluate the improvement of the SDM regression branch on segmentation results to demonstrate the importance of multitask learning. Table 5 shows the segmentation results of removing the SDM branch and retaining the SDM branch. We also use DSC and 95 HD for evaluation. The results show that the model that retains the SDM branch has a higher DSC value and a lower 95 HD in terms of liver and tumor segmentation, indicating that it can better find the tumor location and segment the marginal region.  
Introducing the SSL method for pretraining, the network helps improve the performance of the liver and tumor segmentation model, as shown in Table 5. By pretraining on a large amount of unlabeled data, the model can learn richer feature representations, enabling better generalization on limited labeled data. Experimental results demonstrate that, compared to models without self-supervised pretraining, models trained with self-supervised learning achieve significant improvements in DSC score, 95 HD, and ASD, leading to more accurate target region predictions and smoother boundary segmentation. This indicates that self-supervised pretraining can effectively enhance the model's representation capability, thereby improving liver and tumor segmentation performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{SSL} & \multirow{2}{*}{**AG**} & \multirow{2}{*}{**SDM**} & \multicolumn{2}{c}{**DSC (\%)**} & \multicolumn{2}{c}{**95 HD**} & \multicolumn{2}{c}{**ASD**} \\ \cline{3-8}  & & & **Liver** & **Tumor** & **Liver** & **Tumor** & **Liver** & **Tumor** \\ \hline \multirow{4}{*}{\(\check{\check{\check{\check{\check{\check{\check{\check{\check{\check{\check{\check{ }}}}}}}}}}}\)} & & & \(95.28\pm 3.45\) & \(83.62\pm 3.62\) & \(4.08\pm 2.99\) & \(4.56\pm 1.78\) & \(1.09\pm 0.40\) &  
After introducing the attention gate mechanism, the performance of the liver and tumor segmentation model has significantly improved, as shown in Table 5. Specifically, DSC increased by approximately 0.59% and 1.39% for liver and tumor segmentation, respectively, indicating that the model can more accurately capture the boundaries and shapes of the target regions. Furthermore, 95 HD for liver and tumor decreased by approximately 0.2 and 0.25, respectively, demonstrating a notable enhancement in prediction accuracy for boundary regions, particularly when handling complex and small-sized tumors. These improvements suggest that the attention gate mechanism effectively enhances the model's ability to focus on critical regions, thereby improving segmentation accuracy and robustness.  
Table 6 presents the segmentation results of our model on different CE-MRI images. It is worth noting that the model performed best in the arterial enhanced MRI images. The liver and tumor DSC values reached \(0.968\pm 0.025\) and \(0.878\pm 0.066\), respectively, higher than the performance of the model in the venous phase and delayed phase, which may be attributed to tumors being associated with more excellent contrast and sharper visualization of surrounding areas in the arterial phase. Figure 8 shows the segmentation results of the model for different MRI images of the two different patients. The results showed that CE-MRI in the arterial phase was superior to the other two MRI images, with more complete segmentation results and more accurate edges.  
Finally, to analyze the SDM branch's impact on the segmentation performance, we set \(\alpha\) with different weights for experiments. The results in Table 7 show that with the increase of the weight \(\alpha\), the model performance does not produce a corresponding improvement but produces an inevitable decline. This is mainly reflected in the 95 HD and the ASD of the tumor, indicating that the segmentation performance of the edge part of the model has declined. Finally, when \(\alpha=0.1\), the model achieved the best performance.