期刊
SCIENTIFIC REPORTS
卷 6, 期 -, 页码 -出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/srep33985
关键词
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资金
- National Natural Science Foundation of China [61401263, 61401265, 61273259, 61502290, 61501287, 61573232]
- Industrial Research Project of Science and Technology in Shaanxi Province [2015GY016]
- Natural Science Basic Research Plan in Shaanxi Province of China [2015JQ6228, 2016JQ6056]
- Fundamental Research Funds for the Central Universities of China [GK201402037, GK201503061]
- Six Major Talents Summit of Jiangsu Province [2013-XXRJ-019]
- Natural Science Foundation of Jiangsu Province of China [BK20141482]
- Jiangsu Innovation & Entrepreneurship Group Talents Plan [JS201526]
- National Cancer Institute of the National Institutes of Health [1U24CA199374-01, R21CA167811-01, R21CA179327-01, R21CA195152-01]
- National Institute of Diabetes and Digestive and Kidney Diseases [R01DK098503-02]
- DOD Prostate Cancer Synergistic Idea Development Award [PC120857]
- DOD Lung Cancer Idea Development New Investigator Award [LC130463]
- DOD Prostate Cancer Idea Development Award
- Ohio Third Frontier Technology development Grant
- CTSC Coulter Annual Pilot Grant
- Case Comprehensive Cancer Center Pilot Grant
- VelaSano Grant from the Cleveland Clinic
- Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University
- Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry
Nuclei detection is often a critical initial step in the development of computer aided diagnosis and prognosis schemes in the context of digital pathology images. While over the last few years, a number of nuclei detection methods have been proposed, most of these approaches make idealistic assumptions about the staining quality of the tissue. In this paper, we present a new Multi-Pass Adaptive Voting (MPAV) for nuclei detection which is specifically geared towards images with poor quality staining and noise on account of tissue preparation artifacts. The MPAV utilizes the symmetric property of nuclear boundary and adaptively selects gradient from edge fragments to perform voting for a potential nucleus location. The MPAV was evaluated in three cohorts with different staining methods: Hematoxylin & Eosin, CD31 & Hematoxylin, and Ki-67 and where most of the nuclei were unevenly and imprecisely stained. Across a total of 47 images and nearly 17,700 manually labeled nuclei serving as the ground truth, MPAV was able to achieve a superior performance, with an area under the precision-recall curve (AUC) of 0.73. Additionally, MPAV also outperformed three state-of-the-art nuclei detection methods, a single pass voting method, a multi-pass voting method, and a deep learning based method.
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