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
this https URL

本文介绍了一种高效的 3D 卷积神经网络，采用 3D multi-fiber unit 并使用 3D dilated convolutions 来构建多尺度特征表示，以达到实时密集体积分割的目的，并在 BraTS-2018 挑战数据集上的实验结果表明，该神经网络架构在高保真度的前提下大大降低了计算成本。