The brain tumor segmentation (BraTS) Challenge is an international competition that focuses on the task of automated segmentation of the different parts of brain tumors in magnetic resonance imaging (MRI) scans. U-Net architecture has become the de-facto standard for medical image segmentation tasks, and the proposals based on this architecture have been among the top-ranked solution proposals in the last editions. The 2023 edition of the BraTS challenge introduced a set of 4 new datasets towards addressing additional populations (e.g., sub-Saharan Africa patients) and types of tumors (e.g., meningioma). The goal of this thesis is to train and test different U-Net based architectures using the datasets of the 2023 edition, and to compare and analyse the performance of the different methods both quantitatively and qualitatively.