# CCFNet: Collaborative Cross-Fusion Network for Medical Image Segmentation
## 4 Experiments
### Results  
We evaluate the performance of the CCFNet model on two different types of dataset (Synapse and ACDC) and compare it with various state-of-the-art models. As shown in Table 1, experiments are performed on Synapse using the same image size and preprocessing, and CCFNet is compared with various of the main Transformer- or CNN-based methods such as TransUNet, LeVit-UNet-384, MT-UNet, UCTransNet, TransFuse, and Swin-Unet. Meanwhile, to visually demonstrate the performance of the CCFNet model, some qualitative results of the CCFNet model on Synapse are visually contrasted with a variety of other approaches, such as Swin-Unet, TransUNet, and U-Net. As shown in Figure 5, the red boxes indicate areas where CCFNet outperforms the other methods. Specifically, CCFNet can outperform Swin-Unet by more than 7.08 mm and 2.46% on average HD and DSC, respectively. Among them, CCFNet has the highest DSC in five organs: the stomach, liver, kidney (left), kidney (right), and gallbladder. For some specific organs that are hard to segment, CCFNet can better capture remote dependence. In the first row of Figure 5, CCFNet can better segment the pancreas with long and narrow shapes than other models. In identifying large organs, CCFNet has better accuracy in recognizing and delineating stomach contours, as shown in the second row. The CCFNet segmentation results are primarily compatible with the ground truth labels. When it comes to identifying small organs, CCFNet has certain advantages. As shown in the third row, individual models may not fully identify the gallbladder. CCFNet can identify organ junctions more accurately, as shown in the fourth row, at the junction of the liver and stomach, while the other three models make some errors, which shows that CCFNet is effective. The visualization intuitively demonstrates the high segmentation accuracy of CCFNet, especially on some difficult-to-segment slices. The excellent performance is attributed to the CCFM in CCFNet, which can consider the local small organs while focusing on large organs, showing the strong representation ability of CCFNet in learning low-level specifics and high-level semantic features that are critical in the segmentation of medical images. Table 2 shows the experimental results of ACDC. CCFNet exceeds TransUNet by 1.35% and exceeds Swin-Unet by 1.07% in average DSC score. The excellent performance \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Avg. DSC** & **Avg. HD** & \multirow{2}{*}{**Stomach**} & **Spleen** & **Pancreas** & **Liver** & **Kidney (L)** & **Kidney (R)** & **Gallbladder** & **Aorta** \\  & **(\%)** & **(mm)** & & & & & & & & \\ \hline V-Net [29] & 68.81 & - & 56.98 & 80.56 & 40.05 & 87.84 & 77.10 & 80.75 & 51.87 & 75.34 \\ DARR [51] & 69.77 & - & 45.96 & 89.90 & 54.18 & 94.08 & 72.31 & 73.24 & 53.77 & 74.74 \\ Transuke [25] & 77.42 & - & 73.69 & 87.03 & 57.06 & 94.22 & 80.57 & 78.58 & 63.06 & 85.15 \\ U-Net [3] & 76.85 & 39.70 & 75.58 & 86.67 & 53.98 & 93.43 & 77.77 & 68.60 & 69.72 & 89.07 \\ R50 Net [3] & 74.68 & 36.87 & 74.16 & 85.87 & 56.90 & 93.74 & 80.60 & 78.19 & 63.66 & 87.74 \\ R50 & 75.87 & 36.97 & 74.95 & 87.19 & 49.37 & 93.56 & 79.20 & 72.71 & 63.91 & 55.92 \\ AttnUNet [8] & 77.77 & 36.02 & 75.75 & 87.30 & 58.04 & 93.57 & 77.98 & 71.11 & 68.88 & 89.55 \\ R50V [13] & 71.29 & 32.87 & 73.95 & 81.99 & 45.99 & 91.51 & 75.80 & 72.20 & 55.13 & 73.73 \\ TransUNet [16] & 77.48 & 31.69 & 75.62 & 85.08 & 55.86 & 94.08 & 81.87 & 77.02 & 63.13 & 87.23 \\ Left-U-Net- & 78.83 & 16.84 & 72.76 & 88.86 & 59.07 & 93.11 & 84.61 & 80.25 & 62.23 & 87.33 \\
384 [39] & 78.95 & 26.59 & 76.81 & 87.75 & 59.46 & 93.06 & 81.47 & 77.29 & 64.99 & 87.92 \\ UCTransNet [17] & 78.23 & 26.75 & 79.42 & 87.84 & 56.22 & 93.17 & 80.19 & 73.18 & 66.97 & 88.86 \\ Swin-Unet [28] & 79.13 & 21.55 & 76.60 & 90.66 & 56.58 & 94.29 & 83.28 & 79.61 & 66.53 & 85.47 \\ Ours & 81.59 & 14.47 & 80.47 & 88.19 & 56.89 & 95.37 & 87.42 & 83.50 & 72.49 & 88.35 \\ \hline \hline \end{tabular}
\end{table}