期刊
MEDICAL IMAGE ANALYSIS
卷 65, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.media.2020.101786
关键词
Digital pathology; Nuclei segmentation; Convolutional neural networks
类别
资金
- National Key R&D Program of China [2017YFC1309100]
- National Science Fund for Distinguished Young Scholars [81925023]
- National Natural Science Foundation of China [81771912, 81601469, 81702322]
- National Science Foundation for Young Scientists of China [81701662]
- Science and Technology Planning Project of Guangdong Province [2017B020227012]
- Guangzhou Science and Technology Project of Health [20191A011002]
Nuclei segmentation is a vital step for pathological cancer research. It is still an open problem due to some difficulties, such as color inconsistency introduced by non-uniform manual operations, blurry tumor nucleus boundaries and overlapping tumor cells. In this paper, we aim to leverage the unique optical characteristic of H&E staining images that hematoxylin always stains cell nuclei blue, and eosin always stains the extracellular matrix and cytoplasm pink. Therefore, we extract the Hematoxylin component from RGB images by Beer-Lambert's Law. According to the optical attribute, the extracted Hematoxylin component is robust to color inconsistency. With the Hematoxylin component, we propose a Hematoxylin-aware CNN model for nuclei segmentation without the necessity of color normalization. Our proposed network is formulated as a Triple U-net structure which includes an RGB branch, a Hematoxylin branch and a Segmentation branch. Then we propose a novel feature aggregation strategy to allow the network to fuse features progressively and to learn better feature representations from different branches. Extensive experiments are performed to qualitatively and quantitatively evaluate the effectiveness of our proposed method. In the meanwhile, it outperforms state-of-the-art methods on three different nuclei segmentation datasets. (c) 2020 Elsevier B.V. All rights reserved.
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