# CCFNet: Collaborative Cross-Fusion Network for Medical Image Segmentation
## 4 Experiments
### Analysis and Discussion  
**Feature Analysis.** As shown in Figure 6, in order to verify our motivation and effects, the feature maps of Synapse are visualized using the Grad-CAM method [53]. Due to the limitations of convolution, the region of attention in Figure 6b tends to be locally informative and suffers from a lack of remote information capture. Because of the global features provided by the Transformer branch, Figure 6e learns to activate a larger region than the local region in Figure 6b, indicating that Figure 6e enhances the long-range feature dependence compared to Figure 6b. As the Transformer focuses on extracting global feature dependencies, the region of attention in Figure 6c is insufficient in local feature details. Because of the progressively finer local features captured by the CNN branch, critical local features are retained in Figure 6f. At the same time, the background is significantly suppressed, indicating that the learned feature representation in Figure 6f has a higher local perception ability than in Figure 6c.  
**Defect analysis.** In order to analyze the current shortcomings of CCFNet and improve them in future work, the ground truth of Synapse is compared with the prediction results of CCFNet separately. The red box underscores regions where the performance of CCFNet falls short, as shown in Figure 7. Although the masks of CCFNet-predicted organs are very close to the ground truth labels, there are still some problems. In organ segmentation, CCFNet can sometimes accurately segment organs but cannot accurately identify the corresponding organs. For example, in the first row, in the kidney segmentation, the middle part of the left kidney is identified as the right kidney. Similarly, in the segmentation of  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Method** & **Avg. DSC** & **Avg. HD** & **Stomach** & **Spleen** & **Pancreas** & **Liver** & **Kidney (L)** & **Kidney (R)** & **Gallbladder** & **Aorta** \\  & **(\%)** & **(mm)** & **Stomach** & **Spleen** & **Pancreas** & **Liver** & **Kidney (L)** & **Kidney (R)** & **Gallbladder** & **Aorta** \\ \hline VIT [13] & 67.86 & 36.11 & 70.44 & 81.75 & 42.00 & 91.32 & 74.70 & 67.40 & 45.10 & 70.19 \\ RSO ViT [13] & 71.29 & 32.87 & 73.95 & 81.99 & 45.99 & 91.51 & 75.80 & 72.20 & 55.13 & 73.73 \\ UENTE [18] & 79.56 & 22.97 & 73.99 & 87.81 & 59.25 & 94.46 & 85.66 & 84.80 & 60.56 & 89.99 \\ nnFormer [52] & 86.57 & 10.63 & 86.83 & 90.51 & 83.35 & 96.84 & 86.57 & 86.25 & 70.17 & 92.04 \\ Ours & 86.88 & 8.78 & 85.17 & 89.67 & 82.36 & 97.01 & 85.92 & 90.01 & 72.19 & 92.74 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with 3D module on Synapse (Dice score % for each organ, average Dice score %, and average Hausdorff distance in mm).  
Figure 6: Feature Analysis. (**a**) Ground truth; (**b**,**e**) attention maps of shallow and deep layers in the parallel fusion layer’s CNN branch; (**c**,**f**) attention maps of shallow and deep layers in the parallel fusion layer’s Transformer branch; and (**d**) the attention map finally integrating the CNN and Transformer (best color effect).  
the spleen in the two left images of the second row, part of the spleen is identified as the stomach. In addition, for specific organs, it is challenging to segment organs, such as the pancreas. In the two images on the right-hand side in the second row, CCFNet can segment the pancreas area but cannot accurately describe the pancreas's contour, which indicates that CCFNet still has room for improvement. Processing 2D pictures will lose much spatial information from the original 3D pictures. As shown in the third row, comparing the identification results of two adjacent liver slices. The former slice can accurately segment the liver, but the latter slice cannot segment the liver completely.