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Title: DCAN : Deep contour-aware networks for object instance segmentation from histology images
Authors: Chen, H
Qi, X
Yu, L
Dou, Q
Qin, J 
Heng, PA
Keywords: Deep contour-aware network
Deep learning
Histopathological image analysis
Instance segmentation
Object detection
Transfer learning
Issue Date: 2017
Publisher: Elsevier
Source: Medical image analysis, 2017, v. 36, p. 135-146 How to cite?
Journal: Medical image analysis 
Abstract: In histopathological image analysis, the morphology of histological structures, such as glands and nuclei, has been routinely adopted by pathologists to assess the malignancy degree of adenocarcinomas. Accurate detection and segmentation of these objects of interest from histology images is an essential prerequisite to obtain reliable morphological statistics for quantitative diagnosis. While manual annotation is error-prone, time-consuming and operator-dependant, automated detection and segmentation of objects of interest from histology images can be very challenging due to the large appearance variation, existence of strong mimics, and serious degeneration of histological structures. In order to meet these challenges, we propose a novel deep contour-aware network (DCAN) under a unified multi-task learning framework for more accurate detection and segmentation. In the proposed network, multi-level contextual features are explored based on an end-to-end fully convolutional network (FCN) to deal with the large appearance variation. We further propose to employ an auxiliary supervision mechanism to overcome the problem of vanishing gradients when training such a deep network. More importantly, our network can not only output accurate probability maps of histological objects, but also depict clear contours simultaneously for separating clustered object instances, which further boosts the segmentation performance. Our method ranked the first in two histological object segmentation challenges, including 2015 MICCAI Gland Segmentation Challenge and 2015 MICCAI Nuclei Segmentation Challenge. Extensive experiments on these two challenging datasets demonstrate the superior performance of our method, surpassing all the other methods by a significant margin.
ISSN: 1361-8415
EISSN: 1361-8423
DOI: 10.1016/
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