Unsupervised methods involving active contours or region growing strongly depend on initialization as well. [PERSON] et al. [6] proposed a content-based active contour model that utilized both intensity and texture information to evolve the contour towards the tumor boundary. [PERSON] et al. [7] proposed a quick unsupervised graph-cut algorithm that performed distribution matching and identified tumor boundaries from a single data channel. [PERSON] et al. [8] combined the fuzzy \(c\)-means (FCM) clustering algorithm with spatial region growing to segment the tumors, while [PERSON] et al. [9] proposed a cascade of FCM clustering steps placed into a semi-supervised framework. Supervised learning methods deployed for tumor segmentation first construct a decision model using image-based handcrafted features and use this for prediction in the testing phase. These methods mainly differ from each other in the employed classification technique, the features involved, and the extra processing steps that apply constraints to the intermediary segmentation outcome. [PERSON] et al. [10] extracted so-called multi-fractional Brownian motion features to characterize the tumor texture and compared its performance with Gabor wavelet features, using a modified AdaBoost classifier. [PERSON] et al. [11] built a supervised segmentation procedure by cascading two random forest (RF) classifiers and trained them using first order statistical, geometrical, and asymmetry-based features. They used a Markov random field (MRF)-based segmentation to refine the probability maps provided by the random forests. [PERSON] et al. [12] deployed extremely randomized trees (ERTs) and trained them with features extracted from local intensities and neighborhood context, to perform a hierarchical segmentation of the brain tumor. [PERSON] et al. [13] reformulated the simple linear iterative clustering (SLIC) [14] for the extraction of 3D superpixels, extracted statistical and texton feature from these, and fed them to an RF classifier to distinguish superpixels belonging to tumors and normal tissues. [PERSON] et al. [15] classified the pixels of MRI records with ERTs, using superpixel features from three orthogonal planes. [PERSON] et al. [16] proposed a brain tumor segmentation procedure that combined clustering techniques, region growing, and support vector machine (SVM)-based classification. Supervised learning methods usually provide better segmentation accuracy than unsupervised ones and require a longer processing time. In recent years, convolutional neural networks (CNNs) and deep learning have been attempting to conquer a wide range of application fields in pattern recognition [17]. Features are no longer handcrafted, as these architectures automatically build a hierarchy of increasingly complex features based on the learning data [18]. There are several successful applications in medical image processing, e.g., detection of kidney abnormalities [19], prostate cancer [20], lesions caused by diabetic retinopathy [21] and melanoma [22], and the segmentation of liver [23], cardiac structures [24], colon [25], renal artery [26], mandible [27], and bones [28]. The brain tumor segmentation problem is no exception. In this order, [PERSON] et al. [29] proposed a CNN architecture with small \(3\times 3\) sized kernels and accomplished a thorough analysis of data augmentation techniques for glioma segmentation attempting to compensate the imbalance of classes. [PERSON] et al. [30] applied conditional random fields (CRF) to process the segmentation output produced by a fully convolutional neural network (FCNN) and assure the spatial and appearance consistency. [PERSON] et al. [31] proposed a so-called multi-feature refinement and aggregation scheme for convolutional neural networks that allows for a more effective combination of features and leads to more accurate segmentation. [PERSON] et al.