The brain tumor segmentation is one of the crucial tasks nowadays among other directions and domains where daily clinical workflow requires to put a lot of efforts while studying computer tomography (CT) or structural magnetic resonance imaging (MRI) scans of patients with various pathologies. MRI is the most common method of primary detection, non-invasive diagnostics and a source of recommendations for further treatment of brain diseases. The brain is a complex structure, different areas of which have different functional significance. In this paper, we extend the previous research work on the robust pre-processing methods which allow to consider all available information from MRI scans by the composition of T1, T1C, T2 and T2-Flair sequences in the unique input. Such approach enriches the input data for the segmentation process and helps to improve the accuracy of the segmentation and associated uncertainty evaluation performance. Proposed in this paper method also demonstrates strong improvement on the segmentation problem. This conclusion was done with respect to Dice metric, Sensitivity and Specificity compare to identical training/validation procedure based only on any single sequence and regardless of the chosen neural network architecture. Obtained results demonstrate significant performance improvement while combining three MRI sequences in the 3-channel RGB like image for considered tasks of brain tumor segmentation. In this work we provide the comparison of various gradient descent optimization methods and of the different backbone architectures.