  In this paper, we propose a new image instance segmentation method that
segments individual glands (instances) in colon histology images. This is a
task called instance segmentation that has recently become increasingly
important. The problem is challenging since not only do the glands need to be
segmented from the complex background, they are also required to be
individually identified. Here we leverage the idea of image-to-image prediction
in recent deep learning by building a framework that automatically exploits and
fuses complex multichannel information, regional, location and boundary
patterns in gland histology images. Our proposed system, deep multichannel
framework, alleviates heavy feature design due to the use of convolutional
neural networks and is able to meet multifarious requirement by altering
channels. Compared to methods reported in the 2015 MICCAI Gland Segmentation
Challenge and other currently prevalent methods of instance segmentation, we
observe state-of-the-art results based on a number of evaluation metrics.
