# An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs
## 4 Experiments
### Experimental Performance Comparison and Analysis
Sometimes, because of improper initialization, some neurons of U-Net are unable to be activated during the training process, whereas other neurons are always activated, resulting in network training failure. In the case of nonadversarial training, the accuracy of the segmentation results fluctuates more than it does for other algorithms. In different training instances with the same parameters, the Dice coefficients on the training set and the test set fluctuated by approximately \(\pm 1.6\%\). SegNet is more stable than U-Net, and no training failure occurred for SegNet during the experiment. The results fluctuate by approximately \(\pm 1\%\). Because the GAN model and the AGAN model proposed in this paper are both based on the adversarial approach, these networks are relatively reliable. They showed fluctuations of less than \(\pm 1\%\). The four algorithms are compared below by visualizing the evaluation criteria of the network in the training process. It can be seen from Figure 8 that both GAN and AGAN are based on adversarial networks, and the loss curves are relatively volatile, whereas loss curves of Segnet and U-net are more stable. The loss curve of AGAN drops faster than GAN, and tends to become stable earlier, and the performance in the later period is more stable. The speed of the dice_coeff is also improved faster than GAN. With reference to the results of the dice_coeff in the test set, there is no overfitting in the networks AGAN and GAN. This also confirms that algorithms based on GAN can avoid overfitting. It can also be found from the testing set results that U-net demonstrates a certain overfitting phenomenon. Figure 7: Comparison of three groups of AGAN segmentation results with the results of other methods. There are a total of 247 training and testing images in the JSRT database, which is a relatively small number, and which has only two groups of classifications. To illustrate the reliability of the network, the \"chestX-ray8\" ([[https://www.cc.nih.gov/drd/summers.html](https://www.cc.nih.gov/drd/summers.html)]([https://www.cc.nih.gov/drd/summers.html](https://www.cc.nih.gov/drd/summers.html))), the largest chest X-ray data set, was used as the experimental object. The database comprises 108,948 frontal view X-ray images of 32,717 unique patients. It includes images of atelelectasis, cardiomegaly, effusion, infiltration, mass, nodule, pneumonia and pneumathorax. We randomly selected 122 images from different disease categories and segmented them using different algorithms. Their results are shown in Figure 9.  
Figure 8: The performance comparison of the four networks during the training process. Figure 9: _Cont._  
Figure 9: Comparison of three groups of AGAN segmentation results with the results of other methods. Figure 9 contains three of the experimental results. In the figure, the red line outlines the segmented cardiac area with AGAN, the blue line outlines segmented cardiac area with segnet, the green line outlines the segmented cardiac area with unet, and the purple line outlines segmented cardiac area with GAN. The database does not contain the results of manual segmentation as a reference. However, from the qualitative analysis of the experimental results, it can be concluded that AGAN is effective on X-ray images of non-JSRT data, and most of the results are also shown to be better than the other algorithms. In detail, the cardiac area in the first group of images is almost completely surrounded by the lungs and their edges are clear. All four algorithms have excellent segmentation results in this group of images. In the second group of images, the cardiac area is irregular in shape. In most cases, the AGAN segmentation result is slightly better than other methods. The conclusion is consistent with the experimental conclusion of the JSRT database. In the third group of images, it can be seen that while the segmentation results of the other networks are not desirable, the AGAN still performed well. This shows that the two generators can better extract features.