# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 4 Experiments and Results
### Results on ACDC Segmentation  
The DEIF-Net method was assessed using the ACDC dataset and compared with various other segmentation approaches. The results of these comparisons are detailed in Table 2. DEIF-Net outperformed others in DSC and HD95, achieving values of 92.02% and 1.08 mm, respectively. It also recorded the highest DSC accuracy for ventricular segmentation. In particular, DEIF-Net demonstrated improvements of 2.66% and 1.74% in DSC accuracy over the Unet [1] and Att-Unet [3] models, both of which employ convolutional network architectures. Furthermore, in contrast with Transformer-based models such as Swin-Unet [17] and MISSFormer [19], DEIF-Net showed enhancements of 2.02% and 4.12%, respectively. Additionally, it outperformed the hybrid Transformer and CNN-based models TransUnet [20] and HiFormer [24] by 2.31% and 1.59%, respectively. It is important to note that several methods utilizing hybrid Transformer and CNN architectures did not  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Methods** & **DSC (\%)** \(\uparrow\) & **HD (mm)** \(\downarrow\) & **Aorta** & **Gallbladder** & **Kidney (I)** & **Kidney (R)** & **Liver** & **Pancreas** & **Spleen** & **Stomach** \\ \hline V-Net [2] & 68.81 & - & 75.34 & 51.87 & 77.10 & 80.75 & 87.84 & 40.05 & 80.56 & 56.98 \\ U-net [1] & 76.85 & - & 93.70 & 69.72 & 77.77 & 68.60 & 93.43 & 53.98 &  
demonstrate significant advancements over traditional convolutional networks like Unet and Att-Unet. By contrast, DEIF-Net capitalizes on the synergistic interaction and feature fusion between different encoder types, effectively harnessing the strengths of both, which culminates in marked improvements in segmentation efficacy.  
Further evaluations of DEIF-Net's performance in cardiac organ segmentation were conducted through visual assessments, as depicted in Figure 4. DEIF-Net and other methods were generally successful in accurately segmenting the left ventricle and myocardium. However, the segmentation of the right ventricle posed challenges for several models, which exhibited issues such as incomplete segmentation and non-smooth segmentation boundaries. For example, Att-Unet [3] consistently failed to fully segment the right ventricle in various cases, while HiFormer [24], Swin-Unet [17], and TransUnet [20] struggled with effectively delineating the segmentation edges in multiple instances. In contrast, DEIF-Net not only accomplished complete segmentation of the right ventricle but also excelled at delineating the edges of various tissues, demonstrating reduced misdetection rates compared to the models mentioned.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Methods** & **DSC (\%) \(\uparrow\)** & **HD (mm) \(\downarrow\)** & **RV** & **Myo** & **LV** \\ \hline U-Net [1] & 89.36 & 1.81 & 90.25 & 83.28 & 94.57 \\ Att-UNet [3] & 90.28 & 1.56 & 90.38 & 84.89 & 95.58 \\ TransUNet [20] & 89.71 & 1.16 & 88.86 & 84.53 & 95.73 \\ CTC-Net [23] & 90.77 & - & 90.09 & 85.52 & 96.72 \\ Swin U-Net [17] & 90.00 & 1.32 & 88.55 & 85.62 & 95.83 \\ HiFormer [24] & 90.43 & 1.14 & 88.75 & 87.35 & 95.20 \\ MISFormer [19] & 87.90 & 2.25 & 86.34 & 82.75 & 94.62 \\ DAE-Former [43] & 90.24 & 1.68 & 89.32 & 86.38 & 95.04 \\ TransCeption [21] & 88.73 & 1.72 & 87.68 & 83.87 & 94.66 \\ CS-Unet [44] & 91.37 & - & 89.20 & 89.47 & 95.42 \\ DEIF-Net (ours) & 92.02 & 1.08 & 90.75 & 89.27 & 96.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative results of the developed method on the ACDC dataset, with the second highest performance indicated by blue and the highest performance indicated by red.  
Figure 3: Qualitative results of multiple models on the Synapse dataset, with ground truth, our DEIF-Net, HiFormer, Swin U-Net, TransUnet, Att-Unet, and U-Net visualization indicated by (**a**-**g**), separately.