# Exploiting Global Structure Information to Improve Medical Image Segmentation
## 2 Related Work  
Fully convolutional networks (FCN) represent one of the early deep learning networks for semantic segmentation [18]. In an FCN, the fully connected layers that have been widely used in previous models for image classification such as VGG16 [19] and GoogleNet [20], are replaced by fully convolutional layers, which can take variable-sized images as inputs. The high-level features are combined with their low-level counterparts to obtain more accurate segmentation results. To further enhance the segmentation performance, an encoder-decoder structure that can learn how to upsample input features was proposed, and this architecture is currently widely implemented in various segmentation tasks [21; 22; 23; 24]. In such an encoder-decoder architecture, each part provides certain functionalities. The encoder compresses and extracts the feature information to process the input data, and the decoder uses these compressed features (i.e., representations) to produce the segmentation outputs whose size is the same as that of the input images. For example, the deconvolution network [25] is composed of several decoder layers whose structure resembles the encoder. Furthermore, U-Net is one of the most popular networks based on the encoder-decoder structure for segmentation tasks in the field of medical imaging [4]. This network combines the feature information extracted from the encoder with the outputs of the decoder layers to compensate for the information loss in the downsampling operations in the encoder layers. V-Net [26] is based on the encoder-decoder structure designed to solve the problem of prostate segmentation in 3D medical images. Specifically, residual-based learning is applied to train the V-Net, and a dice loss function is implemented to address the imbalance problem of the foreground and background in 3D medical images. Many researchers have proposed novel architectures to enhance the segmentation performance; however, such architectures do not consider the common structural information of input images as the objective is usually to perform segmentation in natural images. However, in the case of medical images, the degree of standardization is usually relatively large. If a common structure exists in the full image, it is desirable to learn this structural information to obtain more robust and high performance segmentation networks. In the medical imaging field, a key factor to be considered to enhance the segmentation performance is that a segmentation model must learn the prior knowledge regarding the anatomical structure, such as an organ's shape or placement [12]. To this end, [PERSON] et al. [11] used the contour information to train the model by employing the loss function with the contour label, and [PERSON] et al. [27] performed adversarial training for a segmentation model to learn the overall structural information for heart segmentation. In this work, an auxiliary classification network was trained using class labels, and the output of the segmentation network was provided as an input to the trained classifier to update the model parameters in the segmentation network. Through this process, the shared characteristics of the class label and output of the segmentation network could be reflected in the segmentation network. In addition, certain researchers used autoencoders to learn the low-dimensional features for anatomical structures. [PERSON] et al. [12] extracted the feature information by pre-training the autoencoder through segmentation labels. To apply the pre-trained feature information to a segmentation network, the outputs of the segmentation network (i.e., predictions) and segmentation labels were used as the inputs to the encoder part of the pre-trained autoencoder. The outputs of the encoder part (i.e., low-dimensional features) from the two inputs were compared with a loss function to ensure that the distributions of the output values were similar. Similar to [12], [PERSON] et al. [13] adopted a strategy involving a pre-trained autoencoder trained using the ground-truth labels.