Methods for nuclei detection, segmentation, and classification in digital histopathology: A review-current status and future potential

被引：458

作者：

Irshad, Humayun ^{[1
,2
]}

Veillard, Antoine ^{[3
,4
]}

Roux, Ludovic ^{[1
,2
]}

Racoceanu, Daniel ^{[3
,4
]}

机构：

[1] University Joseph Fourier, 38041 Grenoble, France

[2] Image Pervasive Access Lab, Centre National de la Recherch´e Scientifique, Unit Mixed International, Singapore 2955, Singapore

[3] Sorbonne Universit´es, University of Pierre and Marie Curie, Unit Mixed International 2955, 75005 Paris, France

[4] Image and Pervasive Access Laboratory, Singapore

来源：

IEEE Reviews in Biomedical Engineering | 2014年 / 7卷

关键词：

Clinical research - Computer aided diagnosis - Diseases - Pathology;

D O I：

10.1109/RBME.2013.2295804

中图分类号：

学科分类号：

摘要：

Digital pathology represents one of the major evolutions in modern medicine. Pathological examinations constitute the gold standard in many medical protocols, and also play a critical and legal role in the diagnosis process. In the conventional cancer diagnosis, pathologists analyze biopsies to make diagnostic and prognostic assessments, mainly based on the cell morphology and architecture distribution. Recently, computerized methods have been rapidly evolving in the area of digital pathology, with growing applications related to nuclei detection, segmentation, and classification. In cancer research, these approaches have played, and will continue to play a key (often bottleneck) role in minimizing human intervention, consolidating pertinent second opinions, and providing traceable clinical information. Pathological studies have been conducted for numerous cancer detection and grading applications, including brain, breast, cervix, lung, and prostate cancer grading. Our study presents, discusses, and extracts the major trends from an exhaustive overview of various nuclei detection, segmentation, feature computation, and classification techniques used in histopathology imagery, specifically in hematoxylin-eosin and immunohistochemical staining protocols. This study also enables us to measure the challenges that remain, in order to reach robust analysis of whole slide images, essential high content imaging with diagnostic biomarkers and prognosis support in digital pathology. © 2014 IEEE.

引用

页码：97 / 114