# An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs
## 4 Experiments
### Experimental Performance Comparison and Analysis
Among the 122 pictures, four pictures failed to be segmented using AGAN, as shown in the last two figures in Figure 9. These graphs were also unsuccessfully segmented by using the other three methods. Therefore, the AGAN algorithm is reliable. * Stability  
Although the validation set was used to correct overfitting during the training of the above four networks, by comparing the results obtained on the test set and the training set, it can be initially determined that some networks still showed signs of overfitting. The scores (auc_pr + auc_roc) and other evaluation indexes for the four networks are shown in Table 2.  
The best and worst results of the proposed AGAN on the test set are both better than those of the other three networks. The numbers of predictions with Dice coefficients below 80% are three, four, four, and one for the SegNet, U-Net, GAN, and AGAN models, respectively, which explains why cardiac segmentation with the AGAN is less likely to fail. It can also be seen that U-Net and SegNet achieved high scores on the training set but not on the test set. Even with a small amount of data, the difference is already significant. This is consistent with the fact that for these two networks, it cannot be determined during the training process when to stop training and whether there are overfitting problems. Because the GAN and AGAN architectures additionally include a discriminator, making the network more difficult to overfit due to the adversarial training process, their scores are not significantly different between the training set and the test set, and their generalization ability is good. The training curves of these two networks are shown in Figure 8. The AGAN reaches the equilibrium point earlier than the GAN does during training; moreover, the proposed network is more stable after reaching the equilibrium point, and it is not prone to overfitting. Finally, in order to illustrate the stability of the network, the ROC (Receiver Operating Characteristic) curves of the four networks are shown in Figure 7:0 \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Index** & **SegNet** & **U-Net** & **GAN** & **AGAN** \\ \hline STR & 1.95 & 1.96 & 1.93 & 1.91 \\ STE & 1.93 & 1.91 & 1.90 & 1.91 \\ BR & 97.58\% & 97.65\% & 97.38\% & 98.37\% \\ WR & 69.18\% & 60.02\% & 70.18\% & 72.60\% \\ N & 3 & 4 & 4 & 1 \\ \hline \hline \end{tabular}  
* STR = score on training set, STE = score on test set, BR = best result, WR= worst result, N = number of instances with a Dice coefficient of less than 0.8.  
\end{table} 
Table 2: Comparisons of several performance indicators. The horizontal axis in the ROC curve is 1-specificity (false positive rate), while the vertical axis is sensitivity (true positive rate). The calculation formula is shown in formulas (9) and (10). The FN is replaced by the smooth factor. If the smooth factor is small, the ROC curve is difficult to distinguish, so when drawing the ROC curve, the smooth factor takes a larger value. The ROC curves combine the true positive rate and the false positive rate graphically, which can accurately reflect the relationship amongst the various algorithms. The red curve in the figure does not intersect with other curves and is closest to the upper left corner. Therefore, the performance of AGAN is better than the other three networks. However, the ROC curves of the other networks are smoother. This is because AGAN has a selector to make a selection, so it cannot be smoothed on the excessive threshold.