# Exploiting Global Structure Information to Improve Medical Image Segmentation
## 1 Introduction  
Medical image segmentation is aimed at distinguishing the boundaries of lesions or organs in medical images acquired through X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and other techniques. Segmentation results have been used to accomplish valuable clinical objectives in multiple practices, such as tumor detection to enable precise diagnosis and volume analysis to enable treatment planning [1]. Early studies on medical image segmentation adopted the methods such as edge detection, template matching, statistical shape models, and active contours [2]. The rapid development of deep learning has facilitated active research on image segmentation performed using convolutional neural networks (CNNs) [3]. A CNN is trained using images to produce accurate segmentation results by automatically learning hierarchical representations based on multiple stacked layers. Typically, a CNN-based segmentation model consists of an encoder that extracts features from an input image, and a decoder that restores the extracted features to the original image size through upsampling. U-Net [4], as a representative model having the encoder-decoder structure, effectively combines low- and high-level image features with skip-connections. Currently, U-Net and its variants have been widely used for many applications including medical image segmentation, and have outperformed other CNN-based architectures in terms of the segmentation performance [5, 6, 7, 8]. Nevertheless, although CNN-based segmentation networks demonstrate a high prediction performance, these networks produce anatomically abnormal segmentation resultsin medical images in certain cases [9]. Compared to natural images, medical images are relatively well-standardized and contain anatomical structures that can be utilized as informative clues for segmentation. However, such information is not fully used in the learning process of CNN-based segmentation models. Considering these limitations, researchers have attempted to integrate the anatomical prior knowledge into the segmentation process in the medical imaging domain [10]. For example, contour information [11] or a low-dimensional representation of medical images from an autoencoder [12; 13] has been used to learn anatomical priors. Considering these aspects, this study is aimed at establishing a method of learning anatomical structures using an autoencoder such that the prediction of a segmentation network is performed in the learned anatomical feature space. This framework can help enhance the segmentation performance for the region of interest by reflecting the anatomical information in the learning process of the segmentation network. The proposed method differs from the existing approaches that use the anatomical features provided by pre-trained autoencoders [12; 13]. Specifically, a single-stage method, in which the autoencoder is learned jointly with the segmentation network, is used to enhance the segmentation performance and training efficiency. The proposed method was evaluated using the overlap measures including the intersection over union (IOU) and dice similarity coefficient (DSC), and distance measures such as the average contour distance (ACD) and average surface distance (ASD). To verify the effectiveness of the proposed method, we evaluated the performance of lung segmentation on chest X-rays in the widely used public benchmark datasets of the Japanese Society of Radiation Technology (JSRT) and Montgomery County (MC) [14; 15; 16] and the performance of the spinal cord segmentation on MRI pertaining to the ISMRM 2016 Spinal Cord Challenge dataset [17]. Comparative experiments were performed with the existing approaches that adopted pre-trained autoencoders, and the results demonstrated that the proposed method outperformed the comparison targets in the considered medical image segmentation tasks.