  In the isointense stage, the accurate volumetric image segmentation is a
challenging task due to the low contrast between tissues. In this paper, we
propose a novel very deep network architecture based on a densely convolutional
network for volumetric brain segmentation. The proposed network architecture
provides a dense connection between layers that aims to improve the information
flow in the network. By concatenating features map of fine and coarse dense
blocks, it allows capturing multi-scale contextual information. Experimental
results demonstrate significant advantages of the proposed method over existing
methods, in terms of both segmentation accuracy and parameter efficiency in
MICCAI grand challenge on 6-month infant brain MRI segmentation.
