  Accurate image segmentation of the liver is a challenging problem owing to
its large shape variability and unclear boundaries. Although the applications
of fully convolutional neural networks (CNNs) have shown groundbreaking
results, limited studies have focused on the performance of generalization. In
this study, we introduce a CNN for liver segmentation on abdominal computed
tomography (CT) images that shows high generalization performance and accuracy.
To improve the generalization performance, we initially propose an auto-context
algorithm in a single CNN. The proposed auto-context neural network exploits an
effective high-level residual estimation to obtain the shape prior. Identical
dual paths are effectively trained to represent mutual complementary features
for an accurate posterior analysis of a liver. Further, we extend our network
by employing a self-supervised contour scheme. We trained sparse contour
features by penalizing the ground-truth contour to focus more contour
attentions on the failures. The experimental results show that the proposed
network results in better accuracy when compared to the state-of-the-art
networks by reducing 10.31% of the Hausdorff distance. We used 180 abdominal CT
images for training and validation. Two-fold cross-validation is presented for
a comparison with the state-of-the-art neural networks. Novel multiple N-fold
cross-validations are conducted to verify the performance of generalization.
The proposed network showed the best generalization performance among the
networks. Additionally, we present a series of ablation experiments that
comprehensively support the importance of the underlying concepts.
