# Causal Inference-Based Self-Supervised Cross-Domain Fundus Image Segmentation
## 4 Experiments
### Performance Comparison with Prior Methods
#### 4.2.2 Qualitative Analysis  
Figures 6-8 show the visual segmentation results of the proposed method and several open-source methods, i.e., BEAL [11], IOSUDA [12] and MeFDA [14]. As can be seen, while all methods achieve satisfactory OD segmentation due to its clear boundary, our method produces smoother contours. For OC segmentation, the other methods often fail to generate accurate boundaries. For instance, in the second row of the RIM-ONE-r3 results, the predicted segmentation is much smaller than the ground truth, whereas in the fourth row of the REFUGE Validation/Test results, their predictions include regions that do not belong to the OC. In contrast, our method outperforms these approaches in OC segmentation, as demonstrated in the second row of the Drishti-GS results, where the outline and shape of the segmented results are closer to the ground truth.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Drishti-GS**} & \multicolumn{3}{c}{**RIM-ONE-r3**} & \multicolumn{3}{c}{**REFUGE Val**} \\ \cline{2-10}  & \(DI_{cup}\) & \(DI_{disc}\) & \(\delta\) & \(DI_{cup}\) & \(DI_{disc}\) & \(\delta\) & \(DI_{cup}\) & \(DI_{disc}\) & \(\delta\) \\ \hline TD-GAN [35] & 0.747 & 0.924 & 0.117 & 0.728 & 0.853 & 0.118 & - & - & - \\ [PERSON] et al. [36] & 0.851 & 0.959 & 0.093 & 0.755 & 0.852 & 0.109 & - & - & - \\ WGAN [13] & 0.840 & 0.954 & 0.106 & - & - & - & - & - & - \\ [PERSON] et al. [37] & 0.849 & 0.961 & 0.091 & 0.779 & 0.853 & 0.103 & - & - & - \\ OSAL-pixel [10] & 0.851 & 0.962 & 0.089 & 0.778 & 0.854 & 0.097 & 0.869 & 0.932 & 0.059 \\ pOSAL [10] & 0.858 & 0.965 & 0.082 & 0.787 & 0.865 & 0.089 & 0.875 & 0.946 & 0.051 \\ BEAL [11] & 0.862 & 0.961 & 0.084 & 0.810 & 0.898 & 0.090 & 0.852 & 0.948 & 0.055 \\ IOSUDA [12] & 0.775 & 0.940 & 0.091 & 0.723 & 0.907 & 0.095 & 0.829 & 0.954 & 0.057 \\ CFEA [22] & - & - & - & - & - & - & 0.863 & 0.942 & 0.052 \\ MeFDA [14] & 0.866 & 0.959 & 0.082 & **0.821** & 0.909 & 0.087 & 0.880 & 0.956 & 0.049 \\ OADA [23] & 0.873 & 0.965 & 0.085 & 0.816 & 0.904 & 0.094 & 0.885 & 0.952 & **0.044** \\ CSSN (Ours) & **0.876** & **0.971** & **0.081** & 0.818 & **0.922** & **0.083** & **0.885** & **0.958** & 0.049 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative segmentation results for OD and OC on three public datasets. The best results are highlighted in bold.  
Figure 8: Visual comparison on REFUGE Validation/Test dataset. Compared with BEAL [11], IOSUDA [12], IOSUDA [12] and MeFDA [14]. The green contours represent the segmented optic disc, while the blue contours represent the segmented optic cup.  
Figure 6: Visual comparison on Drishti-GS dataset. Compared with BEAL [11], IOSUDA [12] and MeFDA [14]. The green contours represent the segmented optic disc, while the blue contours represent the segmented optic cup.  
Figure 7: Visual comparison on RIM-ONE+3 dataset. Compared with BEAL [11], IOSUDA [12] and MeFDA [14]. The green contours represent the segmented optic disc, while the blue contours represent the segmented optic cup.