DFP-ResUNet:Convolutional Neural Network with a Dilated Convolutional Feature Pyramid for Multimodal Brain Tumor Segmentation

Background and Objective: Manual brain tumor segmentation by radiologists is time consuming and sub-jective. Therefore, fully automatic segmentation of different brain tumor subregions is essential to the treatment of patients. In this paper, we propose a neural network for automatically segmenting the en-hancing tumor (ET), whole tumor (WT), and tumor core (TC) brain tumor subregions. Methods: The network is based on a U-Net with encoding and decoding structure, a residual module, and a spatial dilated feature pyramid (DFP) module, namely, DFP-ResUNet. First, we propose using a spatial DFP module composed of multiple parallel dilated convolution layers to extract the multiscale image features. This spatial DFP structure improves the ability of the neural network to extract and utilize the multiscale image features. Then, we use the residual module to deepen the network structure. Further, we propose using a multiclass Dice loss function to suppress the impact of class imbalance on brain tumor segmentation. We carried out a large number of ablation experiments to verify the feasibility and superiority of our approach using the Multimodal Brain Tumor Segmentation (BraTS) challenge dataset. Results: The mean Dice score of different subregions was ET 0.8431, WT 0.897 and TC 0.9068 using the proposed method on the BraTS 2018 challenge validation set and 0.7985, 0.90281, 0.8453 on the BraTS 2019 challenge, respectively. Further, we got a high Sensitivity and Specificity and low Hausdorff distance. Conclusions: Through the analysis of the experimental results, it can be seen that the proposed approach DFP-ResUNet has a great potential in segmenting different subregions of brain tumors and can be applied in clinical practice. (c) 2021 Published by Elsevier B.V.