Multi-scale brain tumor segmentation combined with deep supervision.

purposeFully convolutional neural networks (FCNNs) have achieved good performance in the field of medical image segmentation. FCNNs that use multimodal images and multi-scale feature extraction have higher accuracy for brain tumor segmentation. Therefore, we have made some improvements to U-Net for fully automated segmentation of gliomas using multimodal images. And we named it multi-scale dilate network with deep supervision (MSD-Net).

methodsMSD-Net is a symmetrical structure composed of a down-sampling process and an up-sampling process. In the down-sampling process, we use the multi-scale feature extraction block (ME) to extract multi-scale features and focus on primary features. Unlike other methods, ME consists of dilate convolution and standard convolution. Dilate convolution extracts multi-scale informations and standard convolution merges features of different scales. Hence, the output of the ME contains local information and global information. During the up-sampling process, we add a deep supervision block (DSB), which can shorten the length of back-propagation. In this paper, we pay more attention to the importance of shallow features for feature restoration.

resultsOur network validated in the BraTS17's validation dataset. The DSC scores of MSD-Net for complete tumor, tumor core and enhancing tumor were 0.88, 0.81 and 0.78, respectively, which outperforms most networks.

conclusionThis study shows that ME enhances the feature extraction ability of the network and improves the accuracy of segmentation results. DSB speeds up the convergence of the network. In addition, we should also pay attention to the contribution of shallow features to feature restoration.