  Accurate and reliable image segmentation is an essential part of biomedical
image analysis. In this paper, we consider the problem of biomedical image
segmentation using deep convolutional neural networks. We propose a new
end-to-end network architecture that effectively integrates local and global
contextual patterns of histologic primitives to obtain a more reliable
segmentation result. Specifically, we introduce a deep fully convolution
residual network with a new skip connection strategy to control the contextual
information passed forward. Moreover, our trained model is also computationally
inexpensive due to its small number of network parameters. We evaluate our
method on two public datasets for epithelium segmentation and tubule
segmentation tasks. Our experimental results show that the proposed method
provides a fast and effective way of producing a pixel-wise dense prediction of
biomedical images.
