# An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs
## 4 Experiments
### Experimental Performance Comparison and Analysis  
Specifically, the cardiac image segmentation results of the AGAN proposed in this paper were compared with the results of three commonly used image segmentation networks: SegNet [28], U-Net [18], and GAN [27]. Their segmentation results on the test set are shown in Table 1. In the following, these results are analyzed from three perspectives. The experimental result and the manually segmentation result dice similarity coefficient (dice_coeff) are the most important indicators to measure their similarity. The range of this value is 0 to 1. The best result is 1 and the worst result is 0. As can be seen from the results displayed in Table 1, the dice_coeff results of the AGAN are superior to those of the other three algorithms. The calculation method of acc in this case is to detect the proportion of the heart region in the segmented image as real heart region. This item can be used to check whether the image is over-segmented. It can be seen from the data in Table 1 that the extent of AGAN over-segmentation is not as significant as other methods. Specificity is the ratio of the cardiac correctly segmented area to all correctly segmented areas. Since the FN part is replaced by the smooth factor in the process of machine learning and testing, the result has a small deviation from the actual FN point. However, the smooth factor calculation rules adopted by each network are the same. The data in Table 1 show that the detection ability of the non-cardiac region is slightly weaker than the other three algorithms. More detailed comparisons and analyses are carried out from the following three aspects. * Accuracy  
Accuracy refers to the consistency between an algorithm's segmentation results and the true segmentation results and is one of the most important indicators for evaluating segmentation algorithms. It can be seen from the experimental results that in terms of accuracy (acc), the network proposed in this paper outperforms the SegNet, U-net, and GAN models. Although the differences between them are not significant, the calculation formula for acc shows that there may be only a few false positive pixels for each of the various methods. In terms of dice_coeff, the result of 94.06% for the network proposed in this paper is 0.9% higher than that for the GAN model and 1.97% and 1.65% higher than those for the other two networks. Due to the small ratio of the area of the heart to the area of the entire \(X\)-ray chest \begin{table}
\begin{tabular}{c c c c c} \hline
**Network** & **Dice\_Coeff** & **Acc** & **Sensitivity** & **Specificity** \\ \hline SegNet & 92.41\% & 98.65\% & 88.86\% & 99.64\% \\ U-Net & 92.09\% & 98.59\% & 88.31\% & 99.64\% \\ GAN & 93.16\% & 98.75\% & 91.58\% & 99.48\% \\ AGAN & 94.06\% & 98.92\% & 92.25\% & 99.60\% \\ \hline \end{tabular}
\end{table} 
Table 1: Comparisons of experimental data from different networks. radiograph for each segmented image, even a 0.9% improvement in dice_coeff is already significant. This can also be seen from Figure 7 and validates the previous assumption. The improvement in dice_coeff is mainly due to the increased sensitivity of segmentation. Since the algorithm proposed in this paper uses two dimensions to extract features, its sensitivity is more than 0.6% higher than that of the other algorithms. By contrast, there is not much difference in the specificity of the algorithms. Figure 7: Cont. * Reliability  
Reliability is considered to assess a segmentation method on the basis of statistical rules. It measures the impact of various changes on a segmentation method. To ensure the wide adaptability of an algorithm, reliability is an important evaluation measure. When the various neural networks above were trained on the same batch of images, their results show fluctuations because of different parameters. U-Net in particular, because it lacks a batch normalization layer, shows a large parameter dependence.