  Automatic and accurate whole-heart and great vessel segmentation from 3D
cardiac magnetic resonance (MR) images plays an important role in the
computer-assisted diagnosis and treatment of cardiovascular disease. However,
this task is very challenging due to ambiguous cardiac borders and large
anatomical variations among different subjects. In this paper, we propose a
novel densely-connected volumetric convolutional neural network, referred as
DenseVoxNet, to automatically segment the cardiac and vascular structures from
3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional
architecture for effective volume-to-volume prediction. From the learning
perspective, our DenseVoxNet has three compelling advantages. First, it
preserves the maximum information flow between layers by a densely-connected
mechanism and hence eases the network training. Second, it avoids learning
redundant feature maps by encouraging feature reuse and hence requires fewer
parameters to achieve high performance, which is essential for medical
applications with limited training data. Third, we add auxiliary side paths to
strengthen the gradient propagation and stabilize the learning process. We
demonstrate the effectiveness of DenseVoxNet by comparing it with the
state-of-the-art approaches from HVSMR 2016 challenge in conjunction with
MICCAI, and our network achieves the best dice coefficient. We also show that
our network can achieve better performance than other 3D ConvNets but with
fewer parameters.
