## 5 Experiment and Results Description
### Experiment and Result Analysis
#### 5.4.2 Experiment with the New Network Architecture  
To verify the segmentation effect of the PDNet network on cardiac images in different positions and directions, location information was added to the LinkNet, ResNet, U-Net and DeepLab networks to form PDNet, and PDNet was used to segment the displaced and rotated images. The model performance was observed, and the Dice coefficient, precision, recall, Dice loss, and F1 score were used to evaluate the performance of the improved network. The losses of the improved PDNet network on the training set were recorded, and the corresponding changes were plotted. Figure 10 shows plots of the loss and MIoU versus the number of training epochs, demonstrating that each network is improved by introducing PDNet during network training. The loss curves in Figure 10 show that PDNet with location information has a stable declining trend during training. The accuracy of the MIoU curve of the model indicates that PDNet is stable and converges, showing the generalizability of the network. Figure 11 shows the cardiac segmentation results of the displaced and rotated X-ray images obtained by PDNet, which is composed of various networks and position information. Figure 10: _Cont._  
Three groups of X-ray images with different rotations and displacements were selected from the test set, and the heart was segmented by PDNet with location information. The cardiac segmentation results are shown in Figure 11. Figure 11a1-a3 shows the segmentation results of the original X-ray image by various networks. Figure 11b1-b3 shows the image displaced by 10 pixels and its segmentation results. Figure 11c1-c3 shows the image displaced by 20 pixels and its segmentation results. Figure 11d1-d3 presents the image obtained by rotating by 10 degrees and its segmentation results. Figure 11e1-e3 depicts the image obtained by rotating by 20 degrees and its segmentation results. The first column shows the original image, and the second column depicts the ground truth or displaced/rotated image, with the white region indicating the cardiac area. The third column shows the cardiac area segmented by PDNet modified based on LinkNet, and the third column presents the cardiac area segmented by PDNet modified based on ResNet. The fourth column shows the cardiac area segmented by PDNet modified based on U-Net, and the fifth column shows the cardiac area segmented by PDNet modified based on DeepLab. Figure 10: Loss and MIoU of the four networks during training. According to the figure, the cardiac image segmentation results obtained by PDNet are based on a variety of network transformations. The accuracy and shape of the segmentation results are similar to those of the ground truth. Compared with the results obtained by the network without modification, PDNet obtains considerably improved results, which shows that PDNet is effective. In particular, for images with larger displacements and rotation angles, compared with the previous experiment, a better model was obtained on the small database, even without data enhancement. The different PDNet models obtain segmentation results that are close to the ground truth after the images are displaced and rotated. The effectiveness of the model is not only shown by a specific network but also by the comparison of the results of PDNet transformed by various networks, which obtain essentially consistent and effective segmentation results. The image shape and more comprehensive data are presented by the data comparison in Table 1.  
Figure 11: _Cont._  
Figure 11: _Cont_. The PQ column represents the parameter quantity of the network. The PMIoU column shows the results of cardiac segmentation obtained from the other literature [50], serving as the basic data for this article. When using these networks in this article, for the convenience of comparison, some parameters have been unified. After parameter unification, the preliminary results are similar to the data in the reference literature, referring to the MIoU column and the PMIoU column for comparison.