## 1 Introduction  
Image processing in combination with artificial intelligence and deep learning techniques is a daily growing field of interest not only from the perspective of the IT industry but also of its fusion with different domains due to its numerous applications. Image analysis is considered a powerful tool in medical diagnosis mainly because of the availability of different types of medical imaging devices, such as CT, PET, SPECT, and MRI (1.5T, 3T, 5T, 7T). The most important benefit of imaging techniques is the diagnostic non-invasiveness that supports the recognition of diseases before they progress to a severe stage where treatment is much more complicated and can be less effective or come too late. Automated systems based on artificial intelligence cannot be a substitute for expert diagnosis; they only provide a tool for better and quicker diagnosis. Medical staff should never fully rely on a solution provided by a machine. The findings from an automated system should always be subject to interpretation by a professional and weighed against other decisions based on medical experience. In this article, we deal with MRI brain imaging and provide an automated system that segments parts of tumors in 3D MRI brain images. Gliomas are the primary types of brain tumors. They come from the astrocytes of the central nervous system. Primary brain tumors are classified according to the WHO (World Health Organization) [1] from grade I to grade IV. Grades I and II are considered low-grade tumors (LGG--low-grade glioma), while grades III and IV are highly malignant and called high-grade glioma (HGG). LGG are harder to detect in automated AI systems. LGG type I is generally benign and tends to remain unobservable and untreated in time. However, LGG type II presents the risk of recurring as a HGG [2], which is a much more severe and advanced phase of the cancer. Rarely, especially if not discovered in time, they can form extracranial metastases. The prognosis of patients with HGG is very poor. Even after a surgery, they tend to reoccur. The overall survival time can be enlarged by one or even two gross-total or sub-total resection surgeries [3]. The databases of MRI images used for brain tumor segmentation usually contain four modalities. The T1 image shows the longitudinal relaxation, while T1c does the same but with a contrast agent. In many cases, the most visible areas of the affected tissue show up on this image. T2 is the transverse relaxation time, and FLAIR fluid-attenuated inversion recovery suppresses the effects of cerebrospinal fluid on the MRI image. Brain tumor segmentation means a frame-by-frame analysis of a 3D MRI image and the classification of every pixel in 2D or voxel in 3D into a class or tumor type category. The different classes to be distinguished are background and brain tissues (considered as non-tumor parts) and tumor tissues, such as edema, enhancing tumor, non-enhancing tumor core, or necrotic tumor. Difficulties influencing stable and rigid segmentation via an automated system are the variety of MRI acquisition protocols and the unstandardized and unnormalized images that are not co-registered, the image intensities present inhomogeneity, and different variations of contrast and other lightning conditions. The resolution and quality of the images also have a great impact on the success of an AI system. In addition, the gliomas can vary in size, location, appearance, and structure; they can appear anywhere in the brain. LGG tumors are much blurrier and may contain only some incipient types of tumor tissue, not showing the most visible tumor core at all. The limited number of collected and annotated MRI images also presents an issue, reducing generalization and limiting the convergence of the training process. Fortunately, the problem of a publicly available dataset is solved by several universities and research centers providing a very thoroughly annotated database collected and upgraded for the BRATS competitions from 2012 until today [4; 5; 6].