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

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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.

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Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce ‘deep learning’ as a technique to improve the objectivity and efficiency of histopathologic slide analysis. Through two examples, prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes, we show the potential of this new methodology to reduce the workload for pathologists, while at the same time increasing objectivity of diagnoses. We found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30–40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention. We conclude that ‘deep learning’ holds great promise to improve the efficacy of prostate cancer diagnosis and breast cancer staging.

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Images Figs. 19-24 Figs. 7-12 Figs. 1-6 Figs. 13-18 Figs. 33-36 Figs. 25-29

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Some new, simple and efficient stereological methods and their use in pathological research and diagnosis.

H J Gundersen, T Bendtsen, L Korbo … (1988)

Stereology is a set of simple and efficient methods for quantitation of three-dimensional microscopic structures which is specifically tuned to provide reliable data from sections. Within the last few years, a number of new methods has been developed which are of special interest to pathologists. Methods for estimating the volume, surface area and length of any structure are described in this review. The principles on which stereology is based and the necessary sampling procedures are described and illustrated with examples. The necessary equipment, the measurements, and the calculations are invariably simple and easy.

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Author and article information

Journal

Journal ID (nlm-ta): J Pathol Inform

Journal ID (iso-abbrev): J Pathol Inform

Journal ID (publisher-id): JPI

Title: Journal of Pathology Informatics

Publisher: Wolters Kluwer - Medknow (India )

ISSN (Print): 2229-5089

ISSN (Electronic): 2153-3539

Publication date Collection: 2019

Publication date (Electronic): 08 March 2019

Volume: 10

Electronic Location Identifier: 9

Affiliations

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

[2 ]Department of Pathology and Laboratory Medicine, Drexel University, College of Medicine, Philadelphia, PA, USA

[3 ]Proscia, Philadelphia, PA, USA

[4 ]Department of Pathology, Moffitt Cancer Center, Tampa, FL, USA

[5 ]3scan, San Francisco, CA, USA

[6 ]University of Pittsburg Medical Center, Pittsburgh, PA, USA

[7 ]Inform Diagnostics, Irving, TX, USA

[8 ]Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA

[9 ]The Ohio State University Medical Center, Columbus, OH, USA

[10 ]Indica Labs, Inc., Corrales, NM, USA

[11 ]Novartis, Novartis Institutes for BioMedical Research, Preclinical Safety, East Hannover, NJ, USA

Author notes

Address for correspondence: Dr. Famke Aeffner, Amgen Inc., 1120 Veterans Blvd, South San Francisco, CA 94080, USA. E-mail: faeffner@ 123456amgen.com

Article

Publisher ID: JPI-10-9

DOI: 10.4103/jpi.jpi_82_18

PMC ID: 6437786

PubMed ID: 30984469

SO-VID: f7fe3616-002d-4444-9d80-813c62ff6f8a

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This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

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

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Abstract

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Most cited references 124

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

Histological Grading and Prognosis in Breast Cancer

Some new, simple and efficient stereological methods and their use in pathological research and diagnosis.

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