Brain tumor segmentation has been a widely researched topic for decades, and it intensified ten years ago as a consequence of the Brain Tumor Segmentation Challenges (BraTS), which provided and yearly updated a standard multi-spectral brain tumor MRI data set and a unified evaluation framework to the research community. This paper proposes a procedure for brain tumor segmentation, which uses a spatial histogram enhancement method to preprocess the data, and two identical cascaded U-net networks that work with 3D convolution. The first U-net accomplishes an intermediary segmentation of the brain volume, while the second one reevaluates the labels given to pixels based on the labels of neighbor pixels. The output of both U-nets are evaluated using statistical accuracy benchmarks. The proposed procedure achieved an average Dice score of 88.8% on the high-grade glioma records of the BraTS 2019 training data set. Post-processing increased the average Dice score by 1.1%, but in case of typical small high-grade tumor lesion it can achieve an improvement of up to 5%.