A Novel Brain Tumor Segmentation Approach Based on Deep Convolutional Neural Network and Level Set

In recent years deep convolutional Neural Network (DCNN) gets a big success in brain tumor segmentation. But there are artifacts in the border region of segmentation results using DCNNs. To solve this question, we propose a hybrid model including DCNNs and traditional segmentation methods. First, we use U-Net and ResU-Net network in coarse segmentation. In order to deepen the network levels and improve the network performance, we add residual module to U-Net and comprise the ResU-Net. Second, we use level set in fine segmentation of tumor boundary. We take the intersection of the coarse segmentation outputs of U-Net and ResU-Net as input of level set module. The aim of taking the intersection of U-Net and ResU-Net outputs is to get better initialization information for the level set algorithm and accelerate the evolution of level set functions. The proposed approach is validated on the BraTS 2018 challenge dataset. The metrics used to evaluate the segmentation results are: Dice, Specificity, Sensitivity, Hausdorff distances (HD). We compare our approach with U-Net, ResU-Net and some other methods. The experimental results indicate our approach is better than some other deep networks.