Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images.
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.
College of Computer Science, Shaanxi Normal University, Xi'an, Shaanxi Province, 710119, China.|Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106-7207, USA.
Cheng
C
Lu
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, T6G 2V4, Canada.
Hongming
H
Xu
Jiangsu Key Laboratory of Big Data Analysis Technique, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Jun
J
Xu
Department of Pathology-Anatomic, University Hospitals Case Medial Center, Case Western Reserve University, Cleveland, OH, 44106-7207, USA.
Hannah
H
Gilmore
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, T6G 2V4, Canada.
Mrinal
M
Mandal
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106-7207, USA.
Anant
A
Madabhushi
D001943:Breast Neoplasms / Q000378:metabolism / Q000473:pathology*; D002467:Cell Nucleus / Q000378:metabolism / Q000473:pathology*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D013194:Staining and Labeling / Q000379:methods
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