## 5 Experiment and Results Description
### Evaluation Indicators  
Many evaluation metrics have been applied in the field of medical image segmentation to evaluate the network prediction results from different perspectives. For CXR segmentation, this paper uses the Dice coefficient (Dice), precision (Precision), recall (Recall), and F1 score (F1 Score) as evaluation indicators. The evaluation indices are calculated as follows. The cardiac area is regarded as a positive sample, and noncardiac areas are regarded as negative samples. According to the real and predicted categories of the sample, the sample is classified as a true positive (TP), false positive (FP), true negative (TN) or false negative (FN) sample.  
(1) Precision: The accuracy was calculated as the probability of a correct prediction (positive samples are predicted as positive (TP) among the samples whose prediction results are positive (positive samples are predicted as positive (TP) and negative samples are predicted as positive (FP)). The precision is formulated as  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{9}\]  
In the cardiac segmentation problem, TP represents the proportion of true heart tissue in the predicted cardiac region.  
(2) Recall: The recall is calculated as the probability of predicting a positive sample (a positive sample is predicted as positive (TP)) among all samples with correct prediction results (positive samples are predicted as positive (TP) and negative samples are predicted as negative (FN)). The recall is formulated as  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{10}\]  
In the CXR segmentation problem, the recall differs from the precision, and the recall is the proportion of samples in all cardiac regions that are successfully predicted.  
(3) F1 Score: The F1 score is the harmonic mean of the precision and recall, which is formulated as follows:  
\[\text{F1 Score}=\frac{2*(\text{Precision}*\text{Recall})}{\text{Precision}+ \text{Recall}} \tag{11}\]  
The precision indicates the probability that the prediction is correct in the predicted cardiac region. The precision is evaluated based on the predicted cardiac area, ignoring the true cardiac area. The recall focuses on how many samples in the real cardiac region are correctly predicted. The recall is evaluated based on the real cardiac region, ignoring the incorrectly predicted samples in the predicted region. Thus, the precision and recall are not sufficiently comprehensive. In cardiac prediction tasks, the precision and recall are equally important. The F1 score treats recall and precision equally, considering both the predicted and real cardiac areas. Thus, the final evaluation results are more comprehensive.  
(4) MIoU (Dice coefficient): The Dice coefficient is an evaluation index that combines the precision and recall. Equation (12) shows that the Dice coefficient considers both the predicted and real cardiac areas. The evaluation results are relatively comprehensive. Therefore, the Dice coefficient was also used as a network evaluation index during the experiments.  
\[\text{MIoU}(\text{GT},\text{PR})=\frac{2*|\text{GT}\cap\text{PR}|}{|\text{GT}|+| \text{PR}|} \tag{12}\]  
In the above formula, GT (ground truth) represents the labelled image, PR (prediction) represents the predicted image, and \(|\text{GT}|\) and \(|\text{PR}|\) are the total number of elements relative to the segmentation task.