Brain Tumor Segmentation Using U-Net 3+

Abstract

Abstract: Themeasurementoftumourextentisadifficulttaskinbraintumourtreatmentplanning and quantitative evaluation. Noninvasive magnetic resonance imaging (MRI) has evolved as a first-line diagnostic method for brain malignancies that does not require ionising radiation. The manualsegmentationofbraintumour extent from3DMRI volumes isatime- consuming jobthatheavilyreliesontheoperator'sknowledge.Inthiscontext,adependablefullyautomatic segmentation approach for brain tumour segmentation is required for accurate tumour extent determination. Inthisworkweoffer a fullyautomatic method for braintumour segmentation, whichis basedonU-Net-baseddeepconvolutionalneuralnetworks.Ourtechnique wastested using the Multimodal Brain Tumor Image Segmentation (BRATS 2018) datasets, which included 220 cases of high-grade brain tumour and 54 cases of low-grade tumour. Cross- validation has demonstrated that our method efficiently obtains promising segmentation.