Cited by 8,908AI in cancer detectionCell Image Analysis TechniquesRadiomics and Machine Learning in Medical Imaging

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QuPath: Open source software for digital pathology image analysis

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Bankhead, Peter, Loughrey, Maurice B., Fernández, José A., Dombrowski, Yvonne, McArt, Darragh G., Dunne, Philip D., McQuaid, Stephen, Gray, Ronan T., Murray, Liam J., Coleman, Helen G., James, Jacqueline A., Salto-Tellez, Manuel, Hamilton, Peter W.·2017·Scientific Reports

DOI 10.1038/s41598-017-17204-5PMC PMC5715110

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Abstract

QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath's flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.

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Figure 1 Illustration of QuPath’s use and functionality. (a) A typical workflow for TMA analysis (here, p53) demonstrates several of QuPath’s main features (left-to-right): Creation of a multi-slide project with automated TMA dearraying, stain estimation, cell detection and feature computation, trainable cell classification, batch processing, and survival analysis. (b) QuPath offers a wide range of additional functionality, including support for whole face tissue sections and fluorescence image analysis, data exchange with existing software and platforms (e.g. ImageJ and MATLAB), scriptable data mining, and rapid generation, visualization and export of spatial, morphological and intensity-based features.CC BY · PubMed Central

Figure 2 Survival analysis of colon cancer cohort based on QuPath automated image analysis. (a–d) Kaplan Meier survival analysis for biomarker scores of TMAs stained for CD3, CD8, p53 and PD-L1. Median cutoffs are applied in all cases, except p53 where two cutoffs were selected by an experienced pathologist to distinguish between aberrant negative, “wild type” (normal) and aberrant positive groups. Representative images showing an original core and QuPath markup image are included below. (e) Kaplan Meier curve showing patient stratification based on median tumor stromal percentage. Representative images show the original images and markup for tumors with a high and low stromal percentage respectively. Green indicates regions classified as stroma, dark red indicates tumor epithelium, while yellow represents other classified tissue or whitespace.CC BY · PubMed Central

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“Slides were scanned with theAperio imagerat20 (cid:2) andtheH-score ofatleast ﬁ verepresentative regions/mouse was analyzed with QuPath software (42).”

Authors: Bankhead, P., Loughrey, M. B., Fernández, J. A., Dombrowski, Y., McArt, D. G., Dunne, P. D., McQuaid, S., Gray, R. T., Murray, L. J., Coleman, H. G., James, J. A., Salto-Tellez, M., & Hamilton, P. W.
Affiliations: Queen's University Belfast (GB)
Corresponding author: Hamilton, Peter W.
ORCID: Bankhead, Peter
Loughrey, Maurice B.
Fernández, José A.
Dombrowski, Yvonne
McArt, Darragh G.
Dunne, Philip D.
McQuaid, Stephen
Gray, Ronan T.
Murray, Liam J.
Coleman, Helen G.
James, Jacqueline A.
Venue: Scientific Reports
Publisher: Springer Science and Business Media LLC
Published: 2017-12-04
Volume: 7
Issue: 1
Pages: 16878-16878
ISSN: 2045-2322 (ISSN-L)
DOI: 10.1038/s41598-017-17204-5
PMC: PMC5715110
OpenAlex: W2952481429
Cited by: 8,908
References: 37
Citations by year: 2026: 1616 · 2025: 1896 · 2024: 1681 · 2023: 1359 · 2022: 1023 · 2021: 704 · 2020: 396 · 2019: 171 · 2018: 49 · 2017: 4
FWCI: 99.74
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Scientific Reports (publishedVersion, cc-by)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
Funders: Invest Northern Ireland (RDO0712612)
Friends of the Cancer Centre
Cancer Research UK (20256, A15333, C37703/A15333, C11512, A20256, C11512/A20256, C50104/A17592, 15333)
Public Health Agency (SPI/3315/06, EAT/4905/13)
Health and Social Care Research and Development Division
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords: QuPath, digital pathology, whole slide image analysis, open source software, tumor detection, biomarker analysis, batch processing, scripting, tissue image analysis
MeSH terms: Algorithms, Colonic Neoplasms, Humans, Image Interpretation, Computer-Assisted, Biomarkers, Tumor, User-Computer Interface, Kaplan-Meier Estimate, Programmed Cell Death 1 Ligand 2 Protein

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