DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

TLDR

The morphology of glands is routinely used by pathologists to assess malignancy in adenocarcinomas, and accurate gland segmentation from histology images is essential for reliable quantitative diagnosis. The study proposes an efficient deep contour‑aware network (DCAN) to accurately segment glands from histology images. DCAN uses a hierarchical architecture that extracts multi‑level contextual features, applies auxiliary supervision and multi‑task regularization, and jointly predicts gland probability maps and clear contours to improve segmentation. DCAN won the 2015 MICCAI Gland Segmentation Challenge, outperforming 13 competing methods by a significant margin.

Abstract

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.