# An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs
## 1 Introduction  
Image segmentation is indispensable for extracting quantitative information on special tissues from images, and it is also a preprocessing step and prerequisite for visualization [1]. Since the 1970s, although great changes have taken place in the diagnostic techniques used in medical imaging due to the widespread application of ultrasound, CT and MRI in clinical examinations, X-ray chest radiographs are still widely used as a simple, feasible and effective inspection method, including in the diagnosis of cardiovascular diseases. The applications of cardiac segmentation in X-ray images include the following three aspects. * Quick initial diagnosis  
For an X-ray chest radiograph, the image can be segmented to measure the cardiothoracic ratio to determine whether the heart is enlarged. This cardiothoracic ratio test is a quick diagnostic method for heart diseases such as rheumatic heart disease, atrial septal defects, tetralogy of Fallot, pericarditis and others [2]. * Joint diagnosisSegmented X-ray chest radiographs can be combined with CT and MRI image reconstruction models to perform multimodal fusion for the quantitative analysis of tissue volume, the localization of diseased tissue, and the local body effect correction of functional imaging data [3]. Guidance for clinical surgery  
The registration of a real-time intraoperative segmented X-ray chest radiograph and a preoperative computed tomographic angiograph can help doctors to study the anatomical structure, guide treatment planning, support navigation, and assist in the completion of thoracic endovascular aortic repair [4]. In short, segmented images are widely used in various situations, such as treatment planning, the local body effect correction of functional imaging data, and computer-guided surgery [5]. With the successful application of medical imaging in clinical medicine, image segmentation is playing an increasingly important role in medical imaging [6]. However, high-resolution (HR) images are not often available without expensive dedicated devices. The task of image segmentation is difficult with respect to the low resolution of X-ray chest radiography and serious tissue interference [7; 8]. Existing research can be divided into three stages. The first stage was primarily developed in the 1960s and 1970s, when rule-based methods were mainly used to extract certain feature points or parts of the cardiopulmonary boundary for image segmentation. [PERSON] and [PERSON] et al. extracted several feature points of the cardiopulmonarymonary boundary in the horizontal direction [9]. [PERSON] and [PERSON] et al. segmented part of the border of the heart and lungs [10]. In the 1990s, [PERSON] et al. used a Fourier shape to match the heart boundary on the basis of boundary detection [11], but still did not obtain the true and complete heart and lung contour. For the segmentation of X-ray chest radiographs, to obtain complete targets, simple rule-based segmentation methods such as the threshold method, boundary detection operators, region growth, and morphological operations can no longer meet current needs [12]. Although the pixel classification method based on feature extraction is more capable of distinguishing different targets, its segmentation results often contain many artifacts, and the amount of calculation is generally large. The second stage lasted from the late 1980s to the early 2000s. In the late 1980s, knowledge-based methods were adopted in the field of medical image processing and analysis, and the rapid development of computer-aided diagnosis and image segmentation technologies made the results of automatic processing more accurate and complete [13]. Since the active modeling approach was proposed, it has attracted the attention of many researchers and has achieved highly successful results in medical image segmentation. It has become one of the mainstream methods of medical image segmentation. Active modeling is a segmentation method based on knowledge constraints.