# DC2 Anet: Generating Lumbar Spine MR Images from CT Scan Data Based on Semi-Supervised Learning
## 2 Literature Review of Medical Imaging Synthesis  
In medical imaging, a number of methods have been proposed for generating one image domain from another, e.g., constructing a CT image from MRI data or a positron emission tomography (PET) image from CT data. Existing methods can be divided into three categories: tissue-segmentation, learning, and atlas-based methods. _Tissue segmentation_ first divides MR image voxels into different tissue classes, such as air, fat, soft tissue, and bone, and then, the segmentation classes are refined manually [5; 9]. However, tissue segmentation is difficult, and its performance strongly depends on segmentation accuracy and the quality of the manual input. _Learning-based methods_ extract features that represent two different domains and then construct a non-linear map between them. However, these methods depend on the quality of the feature extraction in terms of how well they can represent the different domains. Additionally, generating one image domain from another is not as simple as one-to-one mapping [10; 11]. _Atlas-based methods_ apply image registration to align an MR image with an atlas MR image to approximate the correspondence matrix. The matrix can then be used to warp the associated atlas CT image to generate the query CT image [12; 13; 14]. However, the performance of atlas-based methods is closely associated with the registration accuracy for the two image domains. Furthermore, it is difficult to cover pathological differences or significant anatomical variations using atlas data. In recent years, CNNs [15; 16] have demonstrated outstanding performance in various computer vision tasks. In particular, several studies have proven that CNNs are useful in medical imaging [17], such as skin cancer classification [18], X-ray organ segmentation [19], retinal vessel segmentation [20], and brain lesion detection [21]. In these applications, CNN-based medical image synthesis can be considered a form of regression in which non-linear mapping functions are stacked from one image domain to another. For example, Han [22] applied a U-Net [23] architecture consisting of an encoder network and a decoder network in which some layers were connected by skip connections to construct a synthetic CT image from an MR image. To train the deep CNN model using a limited dataset, Han [22] employed transfer learning by initializing the encoder network using a pretrained 16-layer VGG (VGG16) network [24]. The objective function of the network used voxel-wise loss only to minimize the difference between the synthetic and reference images. However, because voxel-wise loss is minimized by averaging all plausible outputs, simply minimizing this loss may produce blurry results. Additionally, the slight voxel-wise misalignment of training data may further lead to a blurry constructed image. Designing objective functions that force the CNN to operate as required, e.g., to generate sharp, realistic, and accurate synthetic images, remains an unsolved problem and generally requires both prior knowledge and experimental observations. In image generation, generative adversarial networks (GANs) [25], which are a form of generative model [26; 27], have been widely employed to produce state-of-the-art, realistic images [28; 29] for applications such as GAN-based image inpainting [30] and video generation [31; 32]. An adversarial loss of GAN learns to satisfy a high-level goal, such as generating an output image indistinguishable from reality. A discriminator network of GAN is used to distinguish whether an image is real or synthesized while simultaneously training a generator network to minimize the adversarial loss. For example, Bi et al. [33] presented a multi-channel GAN with an objective function consisting of adversarial loss and voxel-wise loss to generate a synthetic PET image from a CT image. Similarly, Ben-Cohen et al. [34] independently applied the advantages of a fully-convolutional network (FCN) [35] and a pixel-to-pixel (pix2 pix) model [36] to synthesize a realistic PET image from a CT image.