  The morphology of glands has been used routinely by pathologists to assess
the malignancy degree of adenocarcinomas. Accurate segmentation of glands from
histology images is a crucial step to obtain reliable morphological statistics
for quantitative diagnosis. In this paper, we proposed an efficient deep
contour-aware network (DCAN) to solve this challenging problem under a unified
multi-task learning framework. In the proposed network, multi-level contextual
features from the hierarchical architecture are explored with auxiliary
supervision for accurate gland segmentation. When incorporated with multi-task
regularization during the training, the discriminative capability of
intermediate features can be further improved. Moreover, our network can not
only output accurate probability maps of glands, but also depict clear contours
simultaneously for separating clustered objects, which further boosts the gland
segmentation performance. This unified framework can be efficient when applied
to large-scale histopathological data without resorting to additional steps to
generate contours based on low-level cues for post-separating. Our method won
the 2015 MICCAI Gland Segmentation Challenge out of 13 competitive teams,
surpassing all the other methods by a significant margin.
