## 2 Related Work
### Organ Segmentation  
1. Segmentation of the diaphragm  
Self-supervised learning is used to segment the diaphragm in a four-chamber cardiac X-ray film, and the characteristics of the shared heart and lungs are learned, which improves the accuracy of the model. Subsequently, a three-dimensional convolutional neural network (CNN) was used to further improve the segmentation effect of the model [31]. Three convolutional neural networks (CNNs) are mainly adopted for feature extraction, classification and segmentation in the literature, and finally improve the accuracy and robustness of the model by lung localization, intensity standardization and lung edge removal in the preprocessing stage. Some data augmentation methods are introduced to increase the diversity and quantity of data, in a convolutional neural network (CNN) for automatic segmentation of the diaphragm, thereby improving the generalization performance and stability of the model [32]. (2) Segmentation of the rib  
[PERSON] et al. use a deep neural network with multiple convolutional and pooling layers to extract features from chest X-ray images and then use those features to segment the ribs [33]. To obtain the rib segmentation result, [PERSON] et al. use graph cut to minimize the energy between a binary background model and the rib model [34], and [PERSON] et al. improved automatic rib segmentation in chest radiographs using a structured random forest [35]. (3) Segmentation of the clavicle  
A graph cut model is established using spatial and color features within a specified region of interest after preprocessing the chest radiographs [36]. The rib localization and segmentation are achieved by graph cut optimization. A method based on deep convolutional neural networks (DCNNs) improves the accuracy of the algorithm with dense prediction strategy and receptive field expansion [37]. An improved automatic rib segmentation algorithm using a structured random forest is presented where the precise rib segmentation is achieved through anchor-point localization and morphological operations. It extracts features from the shape and texture of ribs in chest radiographs and establishes a structured random forest classifier [38]. (4) Segmentation of lung fields  
Since the outbreak of COVID-19, automatic segmentation of lung fields for judging the degree of infection through chest radiographs has become a hot topic. [PERSON] et al. establish an automatic lung field segmentation method for chest radiographs using deep learning and transfer learning from ImageNet [39]. The method utilizes a convolutional neural network (CNN) and Mask R-CNN, achieving high segmentation accuracy for both healthy and TB-infected cases. An example in the literature [40] proposed a novel method for lung field segmentation using a deep convolutional neural network (DCNN) with residual connections. The proposed method improved the accuracy and reduced the computational cost using residual connections in the network architecture. Additionally, the literature [41] introduces a modified atrous convolutional neural network (ACNN) for lung field segmentation. The proposed ACNN uses dilated convolutions and multi-scale feature aggregation to improve lung field segmentation performance. (5) Segmentation of heart  
For detecting and segmenting the heart in chest X-ray images, deep convolutional neural network (CNN) architectures are often used [42]. [PERSON], [PERSON] et al. employ an end-to-end training process that utilizes both the image and its corresponding segmentation map to train the network. The framework comprises four pretrained models, namely, ResNet-50, VGG-16, Inception-ResNet-v2, and DenseNet-201. It obtained high accuracies in detecting and segmenting the heart in the chest X-ray images. While in the literature [43], the algorithm using a deep CNN includes two phases: (1) Heart detection, which is a binary classification task; (2) Heart segmentation, which is a pixel-wise classification task.