# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 4 Experiments and Results
### Results on Synapse Multi-Organ Segmentation  
For evaluating the segmentation efficacy of DEFI-Net on multi-organ medical images, we carried out a series of experiments using the Synapse dataset. The performance of DEFI-Net was benchmarked against numerous leading-edge segmentation methods. The comparative analysis included various architectures, such as CNN-based networks (e.g., U-Net [1], Att-UNet [3], Res-UNet [32]) and purely Transformer-based networks (e.g., Swin U-Net [17], TransDeepLab [18], MISSFormer [19]), along with hybrid models employing serial (e.g., TransUNet [20], TransCeption [21]) and parallel CNN-Transformer configurations (e.g., CTC-Net [23], TransFuse [22], HiFormer [24]). As depicted in Table 1, methods such as U-Net and Att-U-Net, primarily based on CNNs, effectively segment the aorta but struggle with organs that exhibit large variations in shape and size. DEFI-Net demonstrated superior segmentation accuracy across multiple organs, achieving the highest average Dice similarity coefficient (DSC) of 83.29% and a competitive 95% Hausdorff distance (HD) of 18.15 mm. This performance surpasses that of both purely CNN-basedand Transformer-based approaches, underscoring the synergistic benefits derived from integrating CNN and Transformer encoders, which significantly enhance segmentation accuracy across most organs. Moreover, DEFI-Net displayed superior segmentation outcomes when compared to hybrid models using both serial and parallel combinations. This highlights the critical role of effective encoder interactions in achieving enhanced model performance. Notably, DEFI-Net exhibited remarkable improvements in the segmentation of the pancreas, spleen, and stomach. The most significant enhancement was observed in the pancreas segmentation, where the DSC increased by 3.02% relative to other evaluated methods. In contrast to models like Swin-UNet [17] and HiFormer [24], DEFI-Net does not rely on pre-trained weights for initializing the Transformer module, further emphasizing its robust feature extraction capabilities and the efficacy of its feature interaction and fusion strategies at each stage.  
Figure 3 presents various methods' segmentation results. It is apparent that conventional convolution-based networks like U-Net [1] and Att-UNet [3] are less effective in accurately identifying and segmenting organs of varying sizes, such as the pancreas and stomach, often leading to under-segmentation or erroneous delineations. Similarly, Swin-UNet [17], TransUNet [20], and HiFormer [24] falter in stomach and pancreatic segmentation, failing to completely delineate the organs, showing issues such as under-segmentation and irregular boundaries. In stark contrast, DEFI-Net exhibits better performance in multi-organ segmentation tasks, accurately identifying and segmenting the pancreas and stomach with more precise, smooth-edged, and complete segmentation areas. These visualization results are consistent with the empirical data, further validating DEFI-Net's substantial improvements in segmenting organs of varying sizes, particularly the pancreas and stomach.