Digital Imaging in Pathology: Whole-Slide Imaging and Beyond

被引：291

作者：

Ghaznavi, Farzad ^{[1
]}

Evans, Andrew

Madabhushi, Anant ^{[2
,3
]}

Feldman, Michael ^{[4
]}

机构：

[1] Univ Toronto, Dept Pathol & Lab Med, Toronto, ON M5S 2S2, Canada

[2] Toronto Gen Hosp, Univ Hlth Network, Lab Med Program, Toronto, ON M5G 2M9, Canada

[3] Rutgers State Univ, Dept Biomed Engn, Lab Computat Imaging & Bioinformat, Piscataway, NJ 08854 USA

[4] Univ Penn, Sch Med, Dept Pathol & Lab Med, Philadelphia, PA 19104 USA

来源：

ANNUAL REVIEW OF PATHOLOGY: MECHANISMS OF DISEASE, VOL 8 | 2013年 / 8卷

关键词：

multispectral imaging; computer-assisted diagnostics; histocytometry; nonlinear dimensionality reduction; VIRTUAL MICROSCOPY; PROSTATE-CANCER; HISTOLOGIC DIAGNOSIS; ANATOMIC PATHOLOGY; AUTOMATED-ANALYSIS; FEATURE-SELECTION; IMMUNOHISTOCHEMISTRY; HISTOPATHOLOGY; QUANTIFICATION; SPECIMENS;

D O I：

10.1146/annurev-pathol-011811-120902

中图分类号：

R36 [病理学];

学科分类号：

100104 ;

摘要：

Digital imaging in pathology has undergone an exponential period of growth and expansion catalyzed by changes in imaging hardware and gains in computational processing. Today, digitization of entire glass slides at near the optical resolution limits of light can occur in 60 s. Whole slides can be imaged in fluorescence or by use of multispectral imaging systems. Computational algorithms have been developed for cytometric analysis of cells and proteins in subcellular locations by use of multiplexed antibody staining protocols. Digital imaging is unlocking the potential to integrate primary image features into high-dimensional genomic assays by moving microscopic analysis into the digital age. This review highlights the emerging field of digital pathology and explores the methods and analytic approaches being developed for the application and use of these methods in clinical care and research settings.

引用

页码：331 / 359

页数：29

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