Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review | IEEE Journals & Magazine | IEEE Xplore

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review


Abstract:

Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and ...Show More

Abstract:

Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.
Published in: IEEE Reviews in Biomedical Engineering ( Volume: 9)
Page(s): 234 - 263
Date of Publication: 06 January 2016

ISSN Information:

PubMed ID: 26742143

Funding Agency:


I. Introduction

Digital pathology and microscopy images play a significant role in decision making for disease diagnosis, since they can provide extensive information for CAD, which enables quantitative analysis of digital images with a high throughput processing rate. Nowadays, automatic digital pathology including image analysis, which can greatly benefit pathologists and patients, has attracted much attention in both research and clinical practice [1], [2]. In comparison with manual assessment that is labor intensive and time consuming, computerized approaches [3]–[6] provide faster and reproducible image analysis such that the basic science researchers and clinician scientists can be released from boring and repeated routine efforts. More importantly, the complex nature of pathology and microscopy images presents significant challenges for manual image analysis, which might lead to large interobserver variations [7]; on the other hand, the CAD can greatly reduce the bias and provide accurate characterization of diseases [8]. In addition, it allows personalized treatments that can significantly benefit the patients. In order to handle large-scale image datasets, grid computing [9]–[11] and computationally scalable algorithms [12]– [14] are reported for high-throughput pathology image analysis. Another advantage of automated methods is that they can easily provide reproducible and rigorous measurements of important image features, which will be used with clinical followup, and thus, allows comparative study and potential prognosis and personalized medicine.

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References

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