ArticleThe journal of pathology. Clinical research2021
Quick Annotator: an open-source digital pathology based rapid image annotation tool.
Article in The journal of pathology. Clinical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Comparing Interactive Machine Learning and Unassisted Segmentation of Geographic Atrophy From Fundus Autofluorescence.Translational vision science & technology · 2026Article
- Translational AI in whole-slide image cancer histopathology: state of the art and regulatory-approved solutions.Frontiers in digital health · 2026Review
- Digital pathology imaging artificial intelligence in cancer research and clinical trials: An NCI workshop report.Journal of pathology informatics · 2026Review
- Development and clinical validation of deep learning-based immunohistochemistry prediction models for subtyping and staging of gastrointestinal cancers.BMC gastroenterology · 2025Article
- Improving the Annotation Process in Computational Pathology: A Pilot Study with Manual and Semi-automated Approaches on Consumer and Medical Grade Devices.Journal of imaging informatics in medicine · 2025Article
- Applications, image analysis, and interpretation of computer vision in medical imaging.Frontiers in radiology · 2025Review
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- SlideTiler: A dataset creator software for boosting deep learning on histological whole slide images.Journal of pathology informatics · 2024Article
- Machine Vision-Detected Peritumoral Lymphocytic Aggregates Are Associated With Disease-Free Survival in Patients With Papillary Thyroid Carcinoma.Laboratory investigation; a journal of technical methods and pathology · 2024Article
- Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets.Journal of imaging informatics in medicine · 2024Review
- Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.Briefings in bioinformatics · 2024Review
- Closing the loop - the role of pathologists in digital and computational pathology research.The journal of pathology. Clinical research · 2024Article
- Effective and efficient active learning for deep learning-based tissue image analysis.Bioinformatics (Oxford, England) · 2023Article
- Accelerating pharmaceutical R&D with a user-friendly AI system for histopathology image analysis.Journal of pathology informatics · 2023Article
- Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation.Journal of pathology informatics · 2023Article
- Towards a national strategy for digital pathology in Switzerland.Virchows Archiv : an international journal of pathology · 2022Article
- A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology.Communications medicine · 2022Article
- Scope2Screen: Focus+Context Techniques for Pathology Tumor Assessment in Multivariate Image Data.IEEE transactions on visualization and computer graphics · 2022Article
- MMO-Net (Multi-Magnification Organ Network): A use case for Organ Identification using Multiple Magnifications in Preclinical Pathology Studies.Journal of pathology informatics · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
Image-based biomarker discovery typically requires accurate segmentation of histologic structures (e.g. cell nuclei, tubules, and epithelial regions) in digital pathology whole slide images (WSIs). Unfortunately, annotating each structure of interest is laborious and often intractable even in moderately sized cohorts. Here, we present an open-source tool, Quick Annotator (QA), designed to improve annotation efficiency of histologic structures by orders of magnitude. While the user annotates regions of interest (ROIs) via an intuitive web interface, a deep learning (DL) model is concurrently optimized using these annotations and applied to the ROI. The user iteratively reviews DL results to either (1) accept accurately annotated regions or (2) correct erroneously segmented structures to improve subsequent model suggestions, before transitioning to other ROIs. We demonstrate the effectiveness of QA over comparable manual efforts via three use cases. These include annotating (1) 337,386 nuclei in 5 pancreatic WSIs, (2) 5,692 tubules in 10 colorectal WSIs, and (3) 14,187 regions of epithelium in 10 breast WSIs. Efficiency gains in terms of annotations per second of 102×, 9×, and 39× were, respectively, witnessed while retaining f-scores >0.95, suggesting that QA may be a valuable tool for efficiently fully annotating WSIs employed in downstream biomarker studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.