DRRNet: Dense Residual Refine Networks for Automatic Brain Tumor Segmentation.

Abstract

Glioma is one of the most common and aggressive brain tumors. Segmentation and subsequent quantitative analysis of brain tumor MRI are routine and crucial for treatment. Due to the time-consuming and tedious manual segmentation, automatic segmentation methods are required for accurate and timely treatment. Recently, segmentation methods based on deep learning are popular because of their self-learning and generalization ability. Therefore, we propose a novel automatic 3D CNN-based method for brain tumor segmentation. In order to better capture the contextual information, we design the network architecture based on u-net and replace the simple skip connection with encoder adaptation blocks. To further improve the performance and reduce computational burden at the same time, we also use dense connected fusion blocks in decoder. We train our model with generalised dice loss function to alleviate the problem of class imbalance. The proposed model is evaluated on the BRATS 2015 testing dataset and obtains dice scores of 0.84, 0.72 and 0.62 for whole tumor, tumor core and enhancing tumor, respectively. Our model is accurate and efficient, achieving results that comparable to the reported state-of-the-art results.