A Multi-Scale Brain Tumor Segmentation Method based on U-Net Network

Abstract

Abstract Brain tumor is a serious threat to human health. Because the size and shape of brain tumors can be uneven, irregular and unstructured. Automatic segmentation of tumors from magnetic resonance imaging (MRI) is a challenging task. Brain tumor segmentation using computer-aided diagnosis has important clinical significance for the prognosis and treatment of patients. The traditional U-Net network can not take full advantage of context information, which is easy to cause the loss of effective information of image. Therefore, we propose a multi-scale segmentation method for brain tumors based on U-Net network, in which a multi-scale module for feature extraction is added between down-sampling and up-sampling. The brain tumor public dataset BraTS2020 is used for testing. Dice coefficient was used as the evaluation index, and our experimental method made the average dice of the whole tumor area, core tumor area and enhanced tumor area reach 87.57%, 86.20% and 84.24%, respectively. The experimental results show that the proposed method has comparable precision with typical brain tumor segmentation methods.