Automatic segmentation of brain tumor in multi-contrast magnetic resonance using deep neural network

Among all the tumors that can affect the brain, gliomas are the most frequent, thus is important to get a correct characterization and delimitation of this malformation to provide the best diagnosis and treatment possible. Nevertheless, there are some issues when dealing with segmenting tumors, it can be a long and tedious labor, which makes it prone to mistakes. To solve those problems, several techniques were proposed, including automatic and semiautomatic segmentation. In this work, we propose the use of a U-net architecture-based deep neural network to automatically realize segmentations of tumors on magnetic resonance brain images obtained from the BRATS 2020 database, which provides T1, T1 contrast enhancement (T1ce), T2, and FLAIR images for each subject. The database has a total of 1476 images distributed in 369 patients, that were shuffled into the training set with 70% of the subjects, and the test set with a percentage of 30%. Our results got a 91.6% DICE value for the validation, from a 91.6% for necrotic core (NET), 91.7% for peritumoral edema (PE), and a 91.4% for enhancing tumor (ET). After the training, we got 55.5%,66.5%, and 68.6% DICE values for NET, PE and ET respectively. We also calculated the whole tumor (WT) segmentation performance, reaching a 78.8% precision and the tumor core (TC) segmentation which reach 75,5% precision.