  Accurate and robust segmentation of abdominal organs on CT is essential for
many clinical applications such as computer-aided diagnosis and computer-aided
surgery. But this task is challenging due to the weak boundaries of organs, the
complexity of the background, and the variable sizes of different organs. To
address these challenges, we introduce a novel framework for multi-organ
segmentation by using organ-attention networks with reverse connections
(OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output
estimates which are combined by statistical fusion exploiting structural
similarity. OAN is a two-stage deep convolutional network, where deep network
features from the first stage are combined with the original image, in a second
stage, to reduce the complex background and enhance the discriminative
information for the target organs. RCs are added to the first stage to give the
lower layers semantic information thereby enabling them to adapt to the sizes
of different organs. Our networks are trained on 2D views enabling us to use
holistic information and allowing efficient computation. To compensate for the
limited cross-sectional information of the original 3D volumetric CT,
multi-sectional images are reconstructed from the three different 2D view
directions. Then we combine the segmentation results from the different views
using statistical fusion, with a novel term relating the structural similarity
of the 2D views to the original 3D structure. To train the network and evaluate
results, 13 structures were manually annotated by four human raters and
confirmed by a senior expert on 236 normal cases. We tested our algorithm and
computed Dice-Sorensen similarity coefficients and surface distances for
evaluating our estimates of the 13 structures. Our experiments show that the
proposed approach outperforms 2D- and 3D-patch based state-of-the-art methods.
