  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 and boundary patterns, with
side supervision (deep supervision on side responses) in gland histology
images. Our proposed system, deep multichannel side supervision (DMCS),
alleviates heavy feature design due to the use of convolutional neural networks
guided by side supervision. Compared to methods reported in the 2015 MICCAI
Gland Segmentation Challenge, we observe state-of-the-art results based on a
number of evaluation metrics.
