Brain Tumor Segmention Based on Dilated Convolution Refine Networks

Abstract

A brain tumor is a growth of abnormal cells in the tissues of the brain, which is difficult for treatment and severely affects patients' cognitive ability. Recent year magnetic resonance imaging (MRI) has been widely used imaging technique to assess brain tumors. However manual segmentation and artificial extracting features block MRI's practice when facing with the huge amount of data produced by MRI. An efficient and automatic image segmentation of brain tumor is still needed. In this paper, a novel automatic segmentation framework of brain tumors, which have 5 parts and resnet-50 use as a backbone, is proposed based on convolutional neural network. A dilated convolution refine (DCR) structure is introduced to extract the local features and global features. After investigating different parameters of our framework, it is proved that DCR is an efficient and robust method in Brain Tumor Segmentation. The experiments are evaluated by Multimodal Brain Tumor Image Segmentation (BRATS 2015) dataset. The results show that our framework in complete tumor segmentation achieved excellent results with a DEC score of 0.87 and a PPV score of 0.92. (GitHub: https://github.com/wei-lab/DCR)