DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

TL;DR

This paper introduces DCAN, a deep neural network that simultaneously segments glands and delineates their contours in histology images, significantly improving accuracy and efficiency for large-scale pathological data analysis.

Contribution

The paper presents a novel multi-task deep learning framework that jointly learns gland segmentation and contour detection, outperforming previous methods in gland segmentation accuracy.

Findings

Won the 2015 MICCAI Gland Segmentation Challenge

Outperformed all competing methods significantly

Efficiently handles large-scale histopathological data

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.