A novel deep learning model DDU-net using edge features to enhance brain tumor segmentation on MR images

Glioma is a relatively common brain tumor disease with high mortality rate. Humans have been seeking a more effective therapy. In the course of treatment, the specific location of the tumor needs to be determined first in any case. Therefore, how to segment tumors from brain tissue accurately and quickly is a persistent problem. In this paper, a new dual-stream decoding CNN architecture combined with U-net for automatic segmentation of brain tumor on MR images namely DDU-net is proposed. Two edge-based optimization strategies are used to enhance the performance of brain tumor segmentation. First, we design a separate branch to process edge stream information. Here, high level edge features are reduced in dimension of channel and integrated into the conventional semantic stream in the way of residual. Second, a regularization loss function is used to encourage the predicted segmentation mask to align with ground truth around the edge mainly by penalizing pixels where the predicted segmentation masks and labels do not match around the edge. In training, we employ a novel edge extraction algorithm for providing edge labels with higher quality. Moreover, we add a self-adaptive balancing class weight coefficient into the cross entropy loss function for solving the serious class imbalance problem in the backpropagation of edge extraction. Our experiments show that this leads to a very efficient architecture which can produce clearer prediction at the edge of the tumor. Our method achieves ideal performance on BraTS2017 and BraTS2018 in terms of Dice coefficient.