## 5 Experimental Results
### Ablation Experiments  
To evaluate the importance and effectiveness of each component in our model and better understand their behaviors and performances, we conducted ablation experiments on the Synapse Abdomen Multi-Organ and ACDC Heart Segmentation datasets, and the results are shown in Tables 3 and 4. It can be seen that adding each module to the model improved the segmentation performance, and applying them to the network together further improved the segmentation ability of our model. From the tables, we can see that our model made progress in the segmentation results of many organs, and the external attention mechanism utilized the correlation between different samples to improve the segmentation performance. The CTrans module can strengthen the fusion of multi-scale feature information, further improving the segmentation performance of the model. On the Synapse dataset, our model achieved a mean DSC (%) of 83.6% and an HD95 (mm) of 13.99, which is a significant improvement compared to the baseline Dae-former model, with an increase of 1.21% in the DSC and a decrease of 3.65 in the HD. On the ACDC dataset, our model improved the average DSC performance by 1.3% and decreased the HD by 0.121, compared to the baseline model. Our model has strong competitiveness compared to both CNN-based models and Transformer-based models. In combination with Figure 8, it can be seen that adding each module improves the accuracy of the segmentation results compared to the baseline model. For example, in the first row and second column, the addition of the CTrans module makes the boundary of the stomach smoother, and the small target, the right kidney, which was not segmented before, became segmented. In the second and fourth columns of the second row, we can see that both the CTrans module and the EA module have improved the boundary determinacy of the left kidney and liver. From the visual comparison of the ablation experiments, we can see that the CTrans and EA modules are dedicated to improving the overall segmentation performance, which may manifest in different aspects of the segmentation, such as the accuracy of segmentation for different organs and the improvement of boundary determinacy and smoothness. As shown in Figure 9, it can be seen on the visualized segmentation map of the ACDC dataset that the baseline model suffers from incorrect segmentation (e.g., RV in the third row) and organ edges that are not smoothed (e.g., LV in the second row and Myo in the first row), but the addition of the CTrans and EA modules significantly improves these phenomena. The segmentation effect occurs when CTrans and EA are added at the same time, resulting in the best segmentation effect. When constructing the DAT, we used the effective attention mechanism and the external attention mechanism. In order to fully combine the advantages of these two attention mechanisms, we also compared three possible connection modes as shown in the figure, namely, sequential cascade (Figure 10a), simple addition fusion (Figure 10b), and complex addition fusion (Figure 10c). The method of a is the scheme we finally choose, which has been introduced above, and the method of b is the simple addition, but this method lacks the normalization operation and leads to instability of the backpropagation. The method of c normalized the output of each attention block and then merged it into the FFN, which achieved good results, but it was still inferior to scheme a. We used the same architecture as the network in Figure 1 and tested all three scenarios on the Synapse dataset, as shown in Table 5. We can see that the sequential fusion approach gave the best results, so we ultimately chose sequential fusion as the base layer of our network. In Table 6, we show the number of parameters of DATUnet. Compared with DAE-Former, we can see that we have a similar number of parameters but a slightly better performance. For network models with a smaller number of parameters, our model has a better Dice score and HD distance.