  Brain tumor segmentation plays a pivotal role in medical image processing. In
this work, we aim to segment brain MRI volumes. 3D convolution neural networks
(CNN) such as 3D U-Net and V-Net employing 3D convolutions to capture the
correlation between adjacent slices have achieved impressive segmentation
results. However, these 3D CNN architectures come with high computational
overheads due to multiple layers of 3D convolutions, which may make these
models prohibitive for practical large-scale applications. To this end, we
propose a highly efficient 3D CNN to achieve real-time dense volumetric
segmentation. The network leverages the 3D multi-fiber unit which consists of
an ensemble of lightweight 3D convolutional networks to significantly reduce
the computational cost. Moreover, 3D dilated convolutions are used to build
multi-scale feature representations. Extensive experimental results on the
BraTS-2018 challenge dataset show that the proposed architecture greatly
reduces computation cost while maintaining high accuracy for brain tumor
segmentation. The source code can be found at
https://github.com/China-LiuXiaopeng/BraTS-DMFNet
