  With the thriving of deep learning, 3D Convolutional Neural Networks have
become a popular choice in volumetric image analysis due to their impressive 3D
contexts mining ability. However, the 3D convolutional kernels will introduce a
significant increase in the amount of trainable parameters. Considering the
training data is often limited in biomedical tasks, a tradeoff has to be made
between model size and its representational power. To address this concern, in
this paper, we propose a novel 3D Dense Separated Convolution (3D-DSC) module
to replace the original 3D convolutional kernels. The 3D-DSC module is
constructed by a series of densely connected 1D filters. The decomposition of
3D kernel into 1D filters reduces the risk of over-fitting by removing the
redundancy of 3D kernels in a topologically constrained manner, while providing
the infrastructure for deepening the network. By further introducing nonlinear
layers and dense connections between 1D filters, the network's representational
power can be significantly improved while maintaining a compact architecture.
We demonstrate the superiority of 3D-DSC on volumetric image classification and
segmentation, which are two challenging tasks often encountered in biomedical
image computing.
