Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review-Current Status and Future Potential

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...

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Bibliographic Details
Published in:IEEE reviews in biomedical engineering 2014, Vol.7, p.97-114
Main Authors: Irshad, Humayun, Veillard, Antoine, Roux, Ludovic, Racoceanu, Daniel
Format: Article
Language:English
Subjects:
Biomedical image processing
Cancer
Cell Nucleus - chemistry
Classification
Digital pathology
Digital systems
Histocytochemistry - methods
histopathology
Humans
Image Processing, Computer-Assisted - methods
Image segmentation
Medical research
microscopic analysis
Microscopy
Microscopy - methods
Neoplasm Grading - methods
Neoplasms - chemistry
nuclei classification
nuclei detection
nuclei segmentation
nuclei separation
Pathology
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Summary: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.
ISSN:1937-3333
1941-1189
DOI:10.1109/RBME.2013.2295804