◎aibioatlasExplore · Compare · Discover
Home›Research Imaging & Structural Analysis›QuPath
research-imagingResearch Imaging & Microscopy
Research Imaging & Structural Analysis · Digital Pathology & Whole-Slide Analysis

QuPath

By University of Edinburgh →

Open-source software for bioimage analysis and digital pathology

QuPath is the open-source standard for whole-slide digital pathology and quantitative microscopy, offering deep learning cell detection, tissue classification, and multiplexed biomarker quantification.

High-performance whole-slide image viewing and annotation across gigapixel filesMachine learning tissue classification (tumor vs stroma vs necrosis)Integration with StarDist and Cellpose for deep learning cell and nuclear segmentationExtensive scripting API in Groovy and Python for automated cohort analysis
Compare

ℹ️ No direct buying or access link is currently recorded. This product remains available for research and comparison.

Report incorrect information or a broken link →

Overview

QuPath is the open-source standard for whole-slide digital pathology and quantitative microscopy, offering deep learning cell detection, tissue classification, and multiplexed biomarker quantification.

Information checked against an official source; not a hands-on test. Source · Last reviewed: 20/09/2026, 11:09:40

Key Features

  • High-performance whole-slide image viewing and annotation across gigapixel files
  • Machine learning tissue classification (tumor vs stroma vs necrosis)
  • Integration with StarDist and Cellpose for deep learning cell and nuclear segmentation
  • Extensive scripting API in Groovy and Python for automated cohort analysis
Interactive 3D Structure

QuPath Predicted Complex

Streams real 3D atomic coordinates from RCSB Protein Data Bank
⇄ Drag to rotate · Scroll to zoom
pLDDT / B-Factor:
>90 Very high 70-90 Confident 50-70 Low <50 Very low

Academic Context & Research Evidence

Biological & Workflow Fit

Biological Application
Oncology biomarker quantification, spatial histology analysis, and clinical trials
Research Workflow
Whole-slide image -> cell segmentation -> phenotyping -> spatial biomarker scoring
Compute & Hardware
Standard desktop / workstation (multi-core CPU, optional GPU for deep learning)
Licensing & Academic Use
GPL-3.0
Documented Evidence
View validation publication / source ↗

Cite this Tool

Use this citation format when referencing QuPath in scientific publications and benchmark papers.

@software{qupath_2026,
  title = {{QuPath}},
  author = {{University of Edinburgh}},
  year = {2026},
  url = {https://qupath.github.io},
  note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}

Peer-Reviewed Literature & Preprints

Live scientific citations streamed from Europe PMC and PubMed for QuPath.

⏳ Fetching real-time literature from Europe PMC & PubMed...

Technical / Product Information

Missing values mean the catalog has no recorded information. They do not mean a feature is absent.

Entry typeOpen Source Software
Access modeOpen Source
AI roleDeep Learning Image Segmentation
Input dataNot recorded
Output dataNot recorded
Licence conditionsGPL-3.0
Commercial eligibilityPermissive open source release
Compute requirementsStandard desktop / workstation (multi-core CPU, optional GPU for deep learning)
ValidationNot recorded
TypeDigital pathology bioimage analysis platform
Intended useNot recorded
CompatibilityNot recorded
ManufacturerUniversity of Edinburgh (Bankhead Lab)
Biological applicationOncology biomarker quantification, spatial histology analysis, and clinical trials
Research workflowWhole-slide image -> cell segmentation -> phenotyping -> spatial biomarker scoring
Evidence levelPeer-reviewed research (Scientific Reports 2017) with thousands of citations
Integration evidencehttps://qupath.github.io
Laboratory handoffCorrelates histological features with genomic data and patient survival curves
AvailabilityAvailable for Windows, macOS, and Linux
Price / accessFree Open Source

Research fit & compatibility

No software–hardware integration has been verified for this entry yet. Explore documented research workflows.

Gallery & resources

No licensed product photograph recorded for QuPath yet. Visit the official resource below for manufacturer imagery.

BEFORE YOU CHOOSE

Evaluate Research Imaging & Structural Analysis

  • Which input, output and scientific task does the selected version support?
  • Is this a model, supporting tool, commercial platform or research prototype?
  • Verify the code, weights, data licence and independent validation before selecting a workflow.
Selection guide →Find alternatives →

Alternatives

Similar entries from the same category.

research-imagingResearch Imaging & Microscopy
Multi-Dimensional Bioimage VisualizationOpen Source

Napari

Open Source Software · Open Source

Fast, interactive multi-dimensional image viewer for Python bioimaging

Chan Zuckerberg Initiative (CZI) & Community

Free Open Source
Explore →＋ Compare
research-imagingResearch Imaging & Microscopy
Cryo-EM Processing PlatformAcademic

cryoSPARC

Scientific Platform · Free Academic / Paid Commercial

High-throughput platform for cryo-EM structural biology and conformational heterogeneity

Structura Biotechnology

Free for academic research / Paid commercial license
Explore →＋ Compare
research-imagingResearch Imaging & Microscopy
Cryo-EM 3D ReconstructionOpen Source

RELION-5

Scientific Software · Open Source

Empirical Bayesian approach for cryo-EM single-particle structure determination

MRC Laboratory of Molecular Biology (Scheres Lab)

Free Open Source
Explore →＋ Compare

FAQ

Where is this product available?

Available for Windows, macOS, and Linux

How is pricing handled?

Prices reflect the source at its last check. Confirm current pricing and regional availability on the official site.

Share this entry with your lab or research teamSpread reproducible science across your network
𝕏 Postin LinkedIn

Peer Reviews & Community Ratings

Feedback from researchers and computational biologists evaluating QuPath.

5.0
★★★★★Based on 0 researcher evaluations
Biological Accuracy
4.8/5
Ease of Installation
4.3/5
Documentation & Code
4.6/5