## 5 Experimentation and Analysis
### Evaluation Indices  
The expression \(Acc\) (Accuracy) is the proportion of correctly categorized pixels to the total pixels in the segmentation result. In image segmentation, we calculate the accuracy between the predicted and true labels of each pixel by comparing them. Specifically, segmentation accuracy is obtained by calculating the number of pixels whose segmentation result is the same as the true result and dividing it by the total number of pixels. A higher accuracy rate means that more pixels are correctly categorized in its segmentation result, and the segmentation performance is better. The formula is as follows:  
\[Acc=\frac{\sum_{i=0}^{k}p_{ii}}{\sum_{i=0}^{k}\sum_{j=0}^{k}p_{ij}} \tag{8}\]  
where \(i\) represents the true value, \(j\) represents the predicted value, \(p_{ij}\) represents the number of pixels that are true for \(i\) but predicted for \(j\), and \(k\) represents the total number of categories. The MIoU (mean intersection over union) is the ratio between the intersection and concatenation between predicted values and labels. In image segmentation, we compare the predicted and labeled values of each category and then calculate the IoU between them. Finally, we can calculate the average of all the IoU categories to get the MIoU of the whole image. The higher the value of MIoU, the closer the segmentation is to the labeled values and the better the segmentation result. The expression of MIoU is given in the following equation:  
\[MIoU=\frac{1}{k+1}\sum_{i=0}^{k}\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}+\sum_{j=0}^ {k}p_{ji}-p_{ii}} \tag{9}\]  
where \(i\) represents the true value, \(j\) represents the predicted value, \(p_{ij}\) represents the number of \(i\) pixels predicted to be \(j\) pixels, and \(k\) represents the number of all species. Dice (Dice coefficient) is the metric of ensemble similarity measure, which is usually used to calculate the similarity of two samples. We compare the predicted values with the labeled values and then calculate the similarity between them. For each category, by comparing the degree of overlap between the predicted segmentation result and the real label, specifically, the ratio of the overlap region between the predicted segmentation \begin{table}
\begin{tabular}{c c} \hline \hline
**Setting** & **Value** \\ \hline Batch size & 4 \\ Crop size & \(512\times 512\) \\ Momentum & 0.9 \\ Initial learning rate & 0.005 \\ Weight decay & 0.000005 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental settings. and the real segmentation to the ratio of the overall size of the two. Finally, we can get the Dice for the whole image. The higher the Dice, the higher the overlap between the predicted segmentation result and the real label, and the better the segmentation will be. The expression is given in the following equation:  
\[Dice=\frac{2\times(\mathit{pred}\cap\mathit{true})}{\mathit{pred}\cup\mathit{ true}} \tag{10}\]  
where _pred_ is the set of predicted values and true is the set of true values, the numerator is the intersection between _pred_ and _true_ multiplied by 2 because there is a reason for double counting the common elements between _pred_ and _true_ in the denominator. The denominator is the concatenation of _pred_ and _true_. Kappa coefficient (Kappa coefficient) is an evaluation metric based on the confusion matrix for pairwise assessment of the consistency between segmentation results and labels. For each category, the consistency between the predicted segmentation results and the ground truth is determined by comparing the predicted segmentation results and the ground truth. Specifically, first, a confusion matrix needs to be constructed based on the predicted results and ground truth of the model. The observed accuracy is then calculated, which can be obtained by calculating the sum of the diagonal elements of the confusion matrix divided by the total number of samples.