Parallel pathway dense neural network with weighted fusion structure for brain tumor segmentation.

Brain tumor segmentation is one of the most challenging tasks for radiomics analysis. Manual segmentation by radiologists is both laborious and subjective, which limits the number of study cases and the reproductivity of each clinical study. Hence, the automatic tumor segmentation method is in high demand, not only to segment the tumor, but also divide it into several fine-grained specific categories in voxel level. To this end, we propose a 3D Center-crop Dense Block for medical images by introducing high-level supervision into the lower layers of neural networks and adopt a parallel pathway network architecture of attention and context pathways. The attention pathway is of normal resolution and mainly concentrate on the detailed information of each voxel, while the context pathway is of low resolution and focus on the surrounding information. Furthermore, we also involve cross-pathway connections from attention pathway to context pathway with weighted fusion structure to compensate the missing detailed information caused by downsampling. The segmentation performance on the BraTS 2015 and BraTS 2017 datasets corroborate the effectiveness of the proposed deep learning architecture over the state-of-the-art results.