# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### Brain Tumor Segmentation:  
Two standard techniques in brain tumor segmentation are patch-based and end-to-end methods. A multi-angle GAN-based framework fuses the synthetic images with the probability maps. The PatchGAN generator focuses on local image patches, randomly selects many fixed-size patches from an image, and normalizes all responses that improve the resultant image. The multichannel structure in the discriminator averages the responses to provide the output [96]. A 3D GAN performs brain tumor segmentation by combining label correction and sample reweighting, where the dual inference network works as a revised label mask generator [97]. The current glioma growth prediction is achieved by mathematical models based on complicated mathematical formulations of partial differential equations with few parameters resulting in insufficient patterns and other characteristics of gliomas. On the contrary, GANs prove the upper hand on mathematical models as they need not directly convert the probability density function to generate data. Plus, GANs can withstand overfitting by providing structured training. A 3D GAN stacks two GANs with conditional initialization of segmented feature maps for glioma growth prediction [98]. Tumor growth prediction needs multiple time points of the same patient's single or multimodal medical images. Again a stacked 3D GAN, GP-GAN, is used for glioma growth prediction [99]. Deep convolutional GAN (DCGAN) first performs data augmentation by generating synthetic images to create a large data set. The image noise is also removed with the help of an adaptive median filter so that the resultant images are of superior features. After this preprocessing step, faster R-CNN uses this synthetic data for training, identifying, and locating tumors. The classification result is tumor placement under three types: meningioma, glioma, pituitary, and primary type [100]. Manual delineation of lesions such as glioma, Ischemic lesions, and Multiple Sclerosis from MR sequences is tedious. Discriminative machine learning techniques such as Random Forest, Support Vector Machines, and DL techniques such as CNN and autoencoders detect and segment lesions from MR scans. However, generative methods such as GANs can also employ convolution operators to learn the distribution parameters [101].  
The class-conditional densities of lesions overlap because the pixel values of ROIs are distributed over the entire intensity range in MR scans. The existence of four major overlapping ROIs (non-enhancing tumor, enhancing, normal, and edema) of intensity  
Figure 6: Grouping of segmentation methods.  
distribution poses a challenge in the segmentation process. Enhancement and segmentation GAN (Enh-Seg-GAN) refines lesion contrast by including the classifier loss in model training, which estimates the central pixel labels of the sliding input patches. The CGAN generator modifies each pixel in the input image patch. It then forwards this to the Markovian discriminator. The synthetic image is concatenated with other fundamental modalities (FLAIR, T1c, and T2) to improve segmentation [102]. Feature concatenation-based squeeze and excitation-GAN (FCSE-GAN) appends the feature concatenation block to the generator network to reduce noise from the image and the squeeze and excitation block to the discriminator network to segment the brain tumor [103].