## 1 Introduction
### Related Work  
The success of deep learning methods in medical imaging has facilitated the use of these techniques in various applications, such as image detection, classification, and segmentation. Given that deep learning is an automatic feature learning method, it has become a major topic in machine learning [28], computer vision [29], and various other areas, including medical diagnosis [30]. Professional knowledge is needed to extract features and apply generalization capability to different domains. Deep learning algorithms automatically learn effective features from the datasets and thus draw an increasing interest in the new era of research. Researchers express that excellent modeling accuracy can be achieved by the deep learning-based deep belief network (DBN) with fewer hidden nodes [31; 32]. The deep belief network (DBN) is a deep learning model that consists of stacking Restricted Boltzmann Machines (RBMs) [31]; RBM is a primary unit of DBN. An RBM has a generative undirected graphical modeling type. It is defined that the lower layer is called a visible layer and a top layer called the hidden layer. The nodes of the visible and hidden layers are stochastic variables and the weights are undirected between visible and hidden layer. The two-step DBN learning consists of unsupervised and supervised. Unsupervised feature learning is implemented by the CD algorithm which trains the stacked RBMs to obtain the initial weight, and supervised learning is implemented by the back-propagation algorithm. Patch-based methods have obtained effective results for segmentation from medical images [33; 34]. These methods label each voxel in the target images by image patch comparison, center on the voxel with patches from an atlas library, and allocate the most probable label in accordance with the closest and best match. Patch-based methods of label fusion have been presented and have given robust segmentation. These methods recently show improved results in different computer vision tasks, including super-resolution [35] and texture synthesis [36]. Image denoising [37] has given a promising performance and led to various patch-based segmentation tool development for the facilitation of medical imaging application methods [33; 34; 38; 39]. In a previous study [40], the corner detection of cervical vertebrae using the patch-based methodology in X-ray images is proposed. This work is extended for cervical verteb segmentation in X-ray images by using a deep fully convolutional neural network (CNN) and has achieved a DICE similarity coefficient of 84% with a shape error of 1.69 mm [41]. In this work, twelve patches around the vertebra are generated to provide the spatial context, and the verteb corners are detected using a machine learning method. A liver segmentation from CT images is presented in [42] from deep stack autoencoder (DSAE). This deep learning-based method learns features from unlabeled data by using autoencoder and fine-tuned these features for segmentation the liver from other abdominal organs. An iterative convolutional neural network based automatic method for vertebrae identification and segmentation is proposed previously [43] to achieve a mean DICE coefficient of 0.948 and a mean surface distance of 0.29 mm. This method used the inherent order of vertebral column to make solve of detection problem and the interactive procedure is utilized for vertebrae identification and segmentation one by one in sequential order. In this method for enabling the contextual information, the vertebra in the low-resolution images is first roughly identified and localized, and afterward reanalyzed in the original high-resolution images to achieve a fine segmentation. A DBN based vertebrae segmentation is proposed in our preliminary work from CT images in [44].