[32] proposed a deep CNN architecture with 3D convolutional kernels and a double pathway learning, which exploits a dense inference technique applied to image segments, thus reducing the computational burden. [PERSON] et al. [33] successfully combined the deep residual networks with the dilated convolution, achieving fine segmentation results. [PERSON] et al. [34] proposed a solution inspired by the concept of generative adversarial networks (GANs). They set up a CNN to produce pixel level labeling, complemented it with an adversarial critic network, and trained them to learn local and global features that were able to capture both short and long distance relationships among pixels. [PERSON] et al. [35] proposed a dual force-based learning strategy, employed the DeepMedic (Version 0.7.0, Biomedical Image Analysis Group, Imperial College London, London, UK, 2018) and U-Net architectures (Version 1.0, Computer Science Department, University of Freiburg, Freiburg, Germany, 2015) for glioma segmentation, and refined the output of deep networks using a multi-layer perceptron (MLP). In general, CNN architectures and deep learning can lead to slightly better accuracy than well designed classical machine learning techniques in the brain tumor detection problem, at the cost of a much higher computational burden in all processing phases, especially at training. The goal of this paper is to propose a solution to the brain tumor segmentation problem that produces a high-quality result with a reduced amount of computation, without needing special hardware that might not be available in underdeveloped countries, employing classification via ensemble learning assisted by several image processing tasks designed for MRI records that may contain focal lesions. Further, the proposed procedure only employs machine learning methods that fully comply with the explainable decision making paradigm, which is soon going to be required by law in the European Union [36]. Multiple pre-processing steps are utilized for bias field reduction, histogram normalization, and atlas-based data enhancement. A twofold post-processing is employed to improve the spatial consistency of the initial segmentation produced by the decision ensemble. Ensemble learning generally combines several classifiers and aggregates their predictions into a final one, thus allowing for more accurate decisions than the individual classifiers are capable of [37; 38]. The ensembles deployed in this study consist of binary decision trees (BDTs) [39]. BDTs are preferred because of their ability to learn any complex patterns that contain no contradiction. Further, with our own implementation of the BDT model, we have full control of its functional parameters. In image segmentation problems, atlases and multiple atlases are generally employed to provide shape or texture priors and to guide the segmentation toward a regularized solution via label fusion [40; 41; 42]. Our solution uses multiple atlases in a different manner: before proceeding to ensemble learning and testing, atlases are trained to characterize the local appearance of normal tissues and applied to transform all feature values to emphasize their deviation from normal. The main contributions consist of: (1) the way the multiple atlases are involved in the pre-processing, to prepare the feature data for segmentation via ensemble learning; (2) the ensemble model built from binary decision trees with unconstrained depth; (3) the two-stage post-processing scheme that discards a large number of false positives. Some components of the procedure proposed here, together with their preliminary results, were presented in previous works. For example, we can mention some part of the feature generation and the classification with ensembles of binary decision trees [43] or the multi-atlas assisted data enhancement of the MRI data [44].