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.

本文提出了一种基于深度学习的多任务学习框架下的高效深度轮廓感知网络（DCAN）用于对组织学图像中的腺体进行精确分割，该方法不仅能输出腺体的准确概率图，还可以同时描述清晰的轮廓以分离聚集的物体。该方法在2015年MICCAI腺体分割挑战赛中胜出，超过了其他13个竞争对手的方法。

DCAN: 深度轮廓感知网络用于准确的腺体分割