# An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs
## 1 Introduction
Three main types of active models have been developed: active contour models (ACMs, or snakes) [14], active shape models (ASMs) [15], and active appearance models (AAMs) [16]. Since ASMs and AAMs were both developed on the basis of snakes, they use valuable prior knowledge in the deformation process, making them suitable for fixed target segmentation. However, when the internal and external structures of the target to be segmented are more complex, the results of the direct application of ASM segmentation are usually not ideal. Reference [17] reported the use of the ASM method to segment the lungs. This method is not effective in segmenting images with relatively clear rib and lung textures. The third stage  
The third phase began with the emergence of AlexNet in 2012. A qualitative leap in accuracy has been achieved for various computer vision tasks; in some cases, the performance can even exceed that of trained humans [18]. Neural networks have a strong spatial recognition capability, enabling the extraction of high-level feature information from the original input. Due to the excellent performance of U-Net, the application of neural networks in the field of medical imaging has increased significantly since 2015. U-Net was proposed by [PERSON] et al. [19] in 2015. This fully convolutional network was first applied for the segmentation of medical images. Due to its strong performance, it was also quickly adopted in other fields. However, U-Net is as prone to overfitting as other networks. By comparison, in a generative adversarial network (GAN), a discriminator is added to allow the network to perform adversarial learning [20], which can reduce overfitting and improve accuracy. The GAN algorithm has been applied for chest contour segmentation in CT images, vascular/optical disc/vision cup segmentation of fundus images, abdominal organ segmentation, microscopic image segmentation, and left ventricle segmentation in echocardiography [21], for which it has been proven to be superior to other algorithms. In medical image segmentation, good results have been achieved by the GAN algorithm, but it also has some problems, to some extent, of unstable robustness due to the selection of the network structure. There is a need to enhance the generalization ability for new types of database [22]. Adaptive dynamic programming (ADP) is a reinforcement learning (RL) scheme to solve the Hamilton-Jacobi-Bellman equation. It demonstrated a strong capability to find the optimal control policy and solve the Bellman equation of continuous-time and discrete-time system forward-in-time. Adaptive frameworks are learning frameworks with great potential characterized by strong abilities of self-learning that have achieved good results in the context of control algorithms [23; 24]. Hence, it would be beneficial in network structure learning. In this paper, an adaptive framework is introduced into the GAN approach. The proposed methods include a generative adversarial model and an adaptive algorithm. The proposed adaptive generative adversarial network (AGAN) includes three parts: a feature extractor, a discriminator, and a selector. The AGAN extracts features of different dimensions from the input images by jointly training multiple generators in the feature extractor. The discriminator scores the extracted features of different dimensions, and in the adaptive learning algorithm, the feature extractor dynamically selects features based on the discriminator scores. Through this selective training, a network with a higher generalization ability and a more accurate feature description ability can be obtained to improve the performance of image segmentation. Segmentation results obtained on the JSRT X-ray chest radiograph database show that the AGAN is superior to other networks and shows better performance in terms of stability.