Brain tumor segmentation with deep convolutional symmetric neural network

Abstract

Gliomas are the most frequent primary brain tumors, which have a high mortality. Surgery is the most commonly used treatment. Magnetic resonance imaging (MRI) is especially useful to assess gliomas and evaluate the success of the treatment in clinical practice. So accurately segmenting the brain tumor from MRI images is the key to clinical diagnostics and treatment planning. However, a large quantity of data produced by MRI prevents manual segmentation in a reasonable time. So automatic approaches are required for quick and effective segmentation. But the spatial and structural variability among brain tumors bring a challenge to automatically segment MRI images. Deep Convolutional Neural Network (DCNN) can achieve complex function mapping. This contributes to response to the challenge. Therefore, DCNN is the commonly used method in brain tumor segmentation. Although a large number of DCNN based methods have been proposed, these methods mainly aim to improve the quality of the image features extracted by DCNN. Actually, these methods usually ignore the prior knowledge in medical images, and the simple prior knowledge in brain tumor segmentation is that most of the tumor regions in images are left-right asymmetry. Based on this, in this paper, a novel deep convolutional neural network which combines symmetry have been proposed to automatically segment brain tumors. Our neural networks, called Deep Convolutional Symmetric Neural Network (DCSNN), extends DCNN based segmentation networks by adding symmetric masks in several layers. Our proposal was validated in the BRATS 2015 database, and we also give some baselines. The results are evaluated by dice similarity coefficient metric (DSC). And our proposed method achieved a competitive result with average DSC of 0.852. Furthermore, our method only takes about 10.8 s to segment a patient case. Although our method is not the best performance in BRATS 2015 challenge, as far as we know, our method outperforms other recent DCNN-based methods.