# CCFNet: Collaborative Cross-Fusion Network for Medical Image Segmentation
## 4 Experiments
### Ablation Studies  
We mainly perform ablation research using the Synapse database to assess the effectiveness of each basic component of CCFNet. All tests are executed with the same hyperparameters and are initiated from scratch to maintain fairness in comparison. Table 4 shows that incorporating the SEConv module into the baseline model results in a consistent gain in segmentation accuracy over the baseline. This is due to the critical role of this module in being able to compensate for the large amount of detail information lost during the upsampling process of the decoder. Adding the CCFM module also brings huge gains, because the CCFM module integrates global and local features to improve segmentation efficiency.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **Avg. DSC (\%)** & **RV** & **Myo** & **LV** \\ \hline R50 UNet [3] & 87.55 & 87.10 & 80.63 & 94.92 \\ R50 AttnUNet [8] & 86.75 & 87.58 & 79.20 & 93.47 \\ R50 ViT [13] & 87.57 & 86.07 & 81.88 & 94.75 \\ UNETR [18] & 88.61 & 85.29 & 86.52 & 94.02 \\ TransUNet [16] & 89.71 & 88.86 & 84.53 & 95.73 \\ Swin-Unet [28] & 90.00 & 88.55 & 85.62 & 95.83 \\ MT-UNet [19] & 90.43 & 86.64 & 89.04 & 95.62 \\ UCTransNet [17] & 89.69 & 87.92 & 85.43 & 95.71 \\ LeViT-UNet-384 [39] & 90.32 & 89.55 & 87.64 & 93.76 \\ Ours & 91.07 & 89.78 & 89.30 & 94.11 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison on the ACDC dataset in DSC (%).  
\begin{table}
\begin{tabular}{c c c} \hline
**Method** & **Params (M)** & **FLOPs (G)** \\ \hline U-Net [3] & 31.13 & 55.84 \\ Swin-Unet [28] & 96.34 & 42.68 \\ TransUNet [16] & 105.32 & 38.52 \\ MT-UNet [19] & 79.07 & 44.72 \\ UCTransNet [17] & 65.6 & 63.2 \\ Ours & 137.36 & 76.18 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of number of parameters and FLOPs for 2D segmentation models in Synapse experiments.  
Table 5 displays the influences of evaluating various fusion techniques within CCFM modules by ablating different components in ablation studies on the Synapse dataset. Using only the DFE module is equivalent to removing the Transformer branch and only using the CNN branch. Using only the CSF module is equivalent to removing the CNN branch and only using the Transformer branch. The experimental results reveal that when removing different branches, the DSC scores on Synapse are almost the same. To some extent, this means that the global information represented by Transformer and the local information represented by the CNN play an equally important role in visual representation, indicating that both global features and local features are important for organ segmentation, and the fusion of the two can help the model to achieve more precise segmentation.  
When only removing the CSF block (using a splicing operation to fuse different features on the two branches), the DSC score on Synapse drops by 2.02%. This shows that the CSF block can reduce the feature semantic difference between two features, introduce convolution-specific inductive bias into the Transformer branch, and enrich the detailed features of the Transformer. When only the SFI block is removed (using splicing operations to fuse different features on the two branches), the DSC score on Synapse drops by 1.22%, and the performance on HD is far worse than the original model. This shows that the SFI block can reduce the spatial information gap between features caused by the asymmetry of extracted features and introduce the global information of the Transformer branch into the CNN branch. The results show that the operations of the CSF block and SFI block are conducive to the fusion between two different feature maps and can integrate the global encoding of the Transformer and the local encoding ability of CNN. This is because the cross-fusion between the CNN to Transformer and Transformer to the CNN can more thoroughly integrate the features between the two different methods than the general simple fusion.