  Objective: A new image instance segmentation method is proposed to segment
individual glands (instances) in colon histology images. This process is
challenging since the glands not only need to be segmented from a complex
background, they must also be individually identified. Methods: We leverage the
idea of image-to-image prediction in recent deep learning by designing an
algorithm that automatically exploits and fuses complex multichannel
information - regional, location, and boundary cues - in gland histology
images. Our proposed algorithm, a deep multichannel framework, alleviates heavy
feature design due to the use of convolutional neural networks and is able to
meet multifarious requirements by altering channels. Results: Compared with
methods reported in the 2015 MICCAI Gland Segmentation Challenge and other
currently prevalent instance segmentation methods, we observe state-of-the-art
results based on the evaluation metrics. Conclusion: The proposed deep
multichannel algorithm is an effective method for gland instance segmentation.
Significance: The generalization ability of our model not only enable the
algorithm to solve gland instance segmentation problems, but the channel is
also alternative that can be replaced for a specific task.
