## 1 Introduction  
By assigning a specific value to each pixel, semantic segmentation localizes the class on an image [1]. Magnetic resonance imaging (MRI) is the most widely used imaging technology to study the brain. The MRI repeats different excitations and produces a contrast image [2]. MRI can be used to diagnose Alzheimer's disease (AD) and multiple sclerosis [3]. One of the common biomarkers for the diagnosis of AD is tissue atrophy [4]. Accurate identification and categorization of brain tissue is crucial for such a diagnosis. To avoid difficult tasks, such as manually analyzing MRI datasets, automatic segmentation techniques are developed [5]. Recently, many deep learning models have been developed for the segmentation of brain tissues [6; 7; 8; 9; 10; 11; 12]. The brain comprises three types of tissues: cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM), as shown in Figure 1. As the brain MRI is a 3D image, it will consume lots of computational resources for the process. We all know that 3D images are a stack of 2D images. When 2D images are stacked, a third dimension is added which will make it 3D. For faster processing with less resources a 2D approach can be used where each slice is processed one by one until all of the slices in the 3D are processed. The result of processing 3D at once or processing and adding each slice to create whole image is the same. Before deep learning, algorithms were implemented for the segmentation purpose. However, it takes a lot of time to obtain results from such processes [8]. Unlike deep learning, the user must change parameters and steps in the algorithm manually as it cannot learn from the data itself. The segmentation of the medical images is developed along with the progress in the deep learning models. Recently, there has been significant development in GAN [13; 14; 15; 16; 17]. GAN is used for a variety of applications, such as generating videos from images [18], increasing resolution [19], and generating an image from text [20]. GAN structures comprise of two parts: a generator and a discriminator [21]. There has been a development of different types of discriminators. Unlike a typical discriminator, which only classifies the output of the generator [22], a new discriminator that discriminates the image on the level of the patch has been introduced [23]. Recent studies have shown that semantic segmentation can be improved with the GAN [22]. Unlike traditional GAN, a multi-discriminator GAN was introduced to distribute the burden for the worker machine [24]. DSC is used as the evaluation metrics for most segmentation tasks. It was first proposed by [PERSON] [25]. In the case of segmentation, DSC is basically calculated by obtaining the intersection area between the output and the ground truth. The value of DSC is always between zero and one. The higher the value of DSC, the better the result will be. To obtain the DSC of the multiclass segmentation, DSC for each class is obtained, and the average is calculated [26]. We can use this evaluation matrix as a loss function during the training too. As deep learning tries to minimize the error while training, we can use the negative of the DSC as the loss function. However, the limitation of the DSC is that it only gives information about the correctly predicted pixels. Motivated by the multiple-discriminator GAN, we propose a novel method to update a GAN alternately with two discriminators, using DSC and a new proposed loss function. We studied the structure of the predicted output and formulated a new function that could give information about areas that are not included as the intersection in the DSC. We hypothesize that instead of using an average of all loss functions at once, a new corrective alternative update of GAN would be able to train the generator properly. In our algorithm, the generator weights will be updated twice in an iteration. The first one will be based on DSC.