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全切片成像中的数字图像分析介绍：数字病理协会白皮书

Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association.

作者信息

Aeffner Famke, Zarella Mark D, Buchbinder Nathan, Bui Marilyn M, Goodman Matthew R, Hartman Douglas J, Lujan Giovanni M, Molani Mariam A, Parwani Anil V, Lillard Kate, Turner Oliver C, Vemuri Venkata N P, Yuil-Valdes Ana G, Bowman Douglas

机构信息

Amgen Inc., Amgen Research, Comparative Biology and Safety Sciences, South San Francisco, CA, USA.

Department of Pathology and Laboratory Medicine, Drexel University, College of Medicine, Philadelphia, PA, USA.

出版信息

J Pathol Inform. 2019 Mar 8;10:9. doi: 10.4103/jpi.jpi_82_18. eCollection 2019.

DOI:10.4103/jpi.jpi_82_18
PMID:30984469
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6437786/
Abstract

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.

摘要

数字病理学中全切片成像的出现推动了通过数字图像分析对组织进行计算机辅助检查的发展。现在，数字化切片可以通过各种算法轻松进行标注和分析。本研究回顾了组织图像分析的基本原理，旨在为病理学家提供有关这些新工具的特征、应用和一般工作流程的基本信息。该综述概述了可用软件解决方案的基本类别、潜在分析策略、技术考量和一般算法输出。讨论了组织图像分析的优点和局限性，并介绍了人工智能和机器学习等新兴概念。最后讨论了数字图像分析工具目前在诊断实验室、转化研究和药物开发中的应用实例。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c0/6437786/701e0caf269d/JPI-10-9-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c0/6437786/5264c209a7fb/JPI-10-9-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c0/6437786/755d01b42602/JPI-10-9-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c0/6437786/b20b935b8c9a/JPI-10-9-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c0/6437786/4f257d049093/JPI-10-9-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c0/6437786/701e0caf269d/JPI-10-9-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c0/6437786/5264c209a7fb/JPI-10-9-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c0/6437786/755d01b42602/JPI-10-9-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c0/6437786/b20b935b8c9a/JPI-10-9-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c0/6437786/4f257d049093/JPI-10-9-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c0/6437786/701e0caf269d/JPI-10-9-g005.jpg

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