  3D left ventricle (LV) segmentation on echocardiography is very important for
diagnosis and treatment of cardiac disease. It is not only because of that
echocardiography is a real-time imaging technology and widespread in clinical
application, but also because of that LV segmentation on 3D echocardiography
can provide more full volume information of heart than LV segmentation on 2D
echocardiography. However, 3D LV segmentation on echocardiography is still an
open and challenging task owing to the lower contrast, higher noise and data
dimensionality, limited annotation of 3D echocardiography. In this paper, we
proposed a novel real-time framework, i.e., VoxelAtlasGAN, for 3D LV
segmentation on 3D echocardiography. This framework has three contributions: 1)
It is based on voxel-to-voxel conditional generative adversarial nets (cGAN).
For the first time, cGAN is used for 3D LV segmentation on echocardiography.
And cGAN advantageously fuses substantial 3D spatial context information from
3D echocardiography by self-learning structured loss; 2) For the first time, it
embeds the atlas into an end-to-end optimization framework, which uses 3D LV
atlas as a powerful prior knowledge to improve the inference speed, address the
lower contrast and the limited annotation problems of 3D echocardiography; 3)
It combines traditional discrimination loss and the new proposed consistent
constraint, which further improves the generalization of the proposed
framework. VoxelAtlasGAN was validated on 60 subjects on 3D echocardiography
and it achieved satisfactory segmentation results and high inference speed. The
mean surface distance is 1.85 mm, the mean hausdorff surface distance is 7.26
mm, mean dice is 0.953, the correlation of EF is 0.918, and the mean inference
speed is 0.1s. These results have demonstrated that our proposed method has
great potential for clinical application
