# Causal Inference-Based Self-Supervised Cross-Domain Fundus Image Segmentation
## 4 Experiments  
### Datasets and Implementation Details  
We validated the proposed method on three cross-domain datasets, with detailed information provided in Table 1. The REFUGE dataset [31] is divided into Train and Validation/Test subsets. The Train set, serving as the source domain, comprises 400 images with OD and OC segmentation annotations acquired using a Zeiss Bisucam 500. In contrast, the Validation/Test set, serving as the target domain, contains 400 training images and 400 testing images captured using a Canon CR-2. Additionally, the Drshti-GS dataset [32] from India includes 101 images with segmentation annotations provided by multiple ophthalmologists. Lastly, the RIM-ONE-r3 dataset [33] from Spain, collected with a Canon EOS 5D, comprises 99 training images and 60 testing images. Because our framework uses self-supervised learning, the target-domain training images are treated as unlabeled data solely for adaptation and pseudo-label generation; their ground-truth masks are not used during training, and only the target-domain test images are employed for final evaluation.  
We construct a dual-path image segmentation network, \(M_{s}\) and \(M_{t}\), using the DeepLabV3+ [26] framework. The network employs MobileNetV2 [27] as its feature extractor. The training is conducted on a server with an Nvidia 1080 ti GPU, over 200 epochs with a batch size of 4. The Adam optimizer [34] is employed with a starting learning rate of \(1\times 10^{-3}\), which is reduced by 0.2 every 100 epochs. To augment the training dataset, we apply a range of random transformations, including random cropping and scaling, random rotation and flipping, elastic deformation, salt-pepper noise, and random region erasing. All augmentation operations are performed online via internal random sampling mechanisms, so that each image receives different augmentation effects in different epochs or iterations.  
We use Dice coefficients (\(DI\)) as a metric to evaluate the segmentation performance of the model. The metric is calculated as:  
\[DI=\frac{2N_{tp}}{2N_{tp}+N_{fp}+N_{fn}}, \tag{12}\]  
where \(N_{tp}\), \(N_{fp}\), and \(N_{fn}\) correspond to the pixel counts for true positives, false positives, and false negatives, respectively.  
In addition, we adopted the absolute value \(\delta\) to evaluate the error between the predicted \(CDR\) (\(CDR_{p}\)) and the ground truth \(CDR\) (\(CDR_{g}\)). The calculation of \(\delta\) is illustrated as follows:  
\[\delta=|CDR_{p}-CDR_{g}| \tag{13}\]  
where \(CDR=VCD/CDD\). \(VCD\) and \(VDD\) are the vertical cup diameter and vertical disc diameter, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Domain** & **Dataset** & \
\begin{tabular}{c} **Number** \\ **(Train/Test)** \\ \end{tabular} & **Image Size** \\ \hline Source & REFUGE Train & 400/0 & 2124 \(\times\) 2056 \\ Target & Drshti-GS & 50/51 & 2047 \(\times\) 1759 \\ Target & RIM-ONE-r3 & 99/60 & 2144 \(\times\) 1424 \\ Target &
\begin{tabular}{c} REFUGE \\ Validation/Test \\ \end{tabular} & 400/400 & 1634 \(\times\) 1634 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The datasets used in the proposed method.