A Containerized Software System for Generation, Management, and Exploration of Features from Whole Slide Tissue Images
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Abstract
Well-curated sets of pathology image features will be critical to clinical studies that aim to evaluate and predict treatment responses. Researchers require information synthesized across multiple biological scales, from the patient to the molecular scale, to more effectively study cancer. This article describes a suite of services and web applications that allow users to select regions of interest in whole slide tissue images, run a segmentation pipeline on the selected regions to extract nuclei and compute shape, size, intensity, and texture features, store and index images and analysis results, and visualize and explore images and computed features. All the services are deployed as containers and the user-facing interfaces as web-based applications. The set of containers and web applications presented in this article is used in cancer research studies of morphologic characteristics of tumor tissues. The software is free and open source. Cancer Res; 77(21); e79–82. ©2017 AACR.
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QuIP is an open-source system that uses the caMicroscope viewer to support the interactive visualization of images, image annotations, and segmentation results as overlays of heatmaps or polygons.
“QuIP is an open-source system that uses the caMicroscope viewer [202] to support the interactive visualization of images, image annotations, and segmentation results as overlays of heatmaps or polygons.”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [201]
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Self-citation
- Authors
- Saltz, J., Sharma, A., Iyer, G., Bremer, E., Wang, F., Jasniewski, A., DiPrima, T., Almeida, J. S., Gao, Y., Zhao, T., Saltz, M., & Kurc, T.
- Affiliations
- Stony Brook University (US)
- Emory University (US)
- 1Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York.
- Corresponding author
- Saltz, Joel
- Venue
- Cancer Research
- Published
- 2017-10-31
- Volume
- 77
- Issue
- 21
- Pages
- e79-e82
- ISSN
- 0008-5472 (ISSN-L)
- 1538-7445
- PMC
- PMC5987533
- OpenAlex
- W2766131660
- Cited by
- 60
- References
- 12
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 8 · 2023: 15 · 2022: 11 · 2021: 4 · 2020: 9 · 2019: 5 · 2018: 2 · 2017: 1
- FWCI
- 2.77
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Cancer Research (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NCI (1U24CA180924-01A1)
- NLM (R01LM011119-01, R01LM009239)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- whole slide images, nuclei segmentation, containerized applications, web application, feature extraction, texture features, image indexing, tumor tissue analysis, open source software
- MeSH terms
- Humans, Image Interpretation, Computer-Assisted, Neoplasms, Software, User-Computer Interface, Internet
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