Edge U-Net: Brain tumor segmentation using MRI based on deep U-Net model with boundary information

Abstract

Blood clots in the brain are frequently caused by brain tumors. Early detection of these clots has the potential to significantly lower morbidity and mortality in cases of brain cancer. It is thus indispensable for a proper brain tumor diagnosis and treatment that tumor tissue magnetic resonance images (MRI) be accurately segmented. Several deep learning approaches to the segmentation of brain tumor MRIs have been proposed, each having been designed to properly map out ‘boundaries’ and thus achieve highly accurate segmentation. This study introduces a deep convolution neural network (DCNN), named the Edge U-Net model, built as an encoder-decoder structure inspired by the U-Net architecture. The Edge U-Net model can more precisely localise tumors by merging boundary-related MRI data with the main data from brain MRIs. In the decoder phase, boundary-related information from original MRIs of different scales is integrated with the appropriate adjacent contextual information. A novel loss function was added to this segmentation model to improve performance. This loss function is enhanced with boundary information, allowing the learning process to produce more precise results. In the conducted experiments, a public dataset with 3064 T1-Weighted Contrast Enhancement (T1-CE) images of three well-known brain tumor types were used. The experiment demonstrated that the proposed framework achieved satisfactory Dice score values compared with state-of-art models, with highly accurate differentiation of brain tissues. It achieved Dice scores of 88.8 % for meningioma, 91.76 % for glioma, and 87.28 % for pituitary tumors. Computations of other performance metrics such as the Jaccard index, sensitivity, and specificity were also conducted. According to the results, the Edge U-Net model is a potential diagnostic tool that can be used to help radiologists more precisely segment brain tumor tissue images.