  Segmentation of the left atrial chamber and assessing its morphology, are
essential for improving our understanding of atrial fibrillation, the most
common type of cardiac arrhythmia. Automation of this process in 3D gadolinium
enhanced-MRI (GE-MRI) data is desirable, as manual delineation is
time-consuming, challenging and observer-dependent. Recently, deep
convolutional neural networks (CNNs) have gained tremendous traction and
achieved state-of-the-art results in medical image segmentation. However, it is
difficult to incorporate local and global information without using contracting
(pooling) layers, which in turn reduces segmentation accuracy for smaller
structures. In this paper, we propose a 3D CNN for volumetric segmentation of
the left atrial chamber in LGE-MRI. Our network is based on the well known
U-Net architecture. We employ a 3D fully convolutional network, with dilated
convolutions in the lowest level of the network, and residual connections
between encoder blocks to incorporate local and global knowledge. The results
show that including global context through the use of dilated convolutions,
helps in domain adaptation, and the overall segmentation accuracy is improved
in comparison to a 3D U-Net.
