  3D segmentation with deep learning if trained with full resolution is the
ideal way of achieving the best accuracy. Unlike in 2D, 3D segmentation
generally does not have sparse outliers, prevents leakage to surrounding soft
tissues, at the very least it is generally more consistent than 2D
segmentation. However, GPU memory is generally the bottleneck for such an
application. Thus, most of the 3D segmentation applications handle sub-sampled
input instead of full resolution, which comes with the cost of losing precision
at the boundary. In order to maintain precision at the boundary and prevent
sparse outliers and leakage, we designed ComboNet. ComboNet is designed in an
end to end fashion with three sub-network structures. The first two are
parallel: 2D UNet with full resolution and 3D UNet with four times sub-sampled
input. The last stage is the concatenation of 2D and 3D outputs along with a
full-resolution input image which is followed by two convolution layers either
with 2D or 3D convolutions. With ComboNet we have achieved $92.1\%$ dice
accuracy for aorta segmentation. With Combonet, we have observed up to $2.3\%$
improvement of dice accuracy as opposed to 2D UNet with the full-resolution
input image.
