Optimized Whole-Slide-Image H&E Stain Normalization: A Step Towards Big Data Integration in Digital Pathology
In the medical diagnostics domain, pathology and histology are pivotal for the precise identification of diseases. Digital histopathology, enhanced by automation, facilitates the efficient analysis of massive amount of biopsy images produced on a daily basis, streamlining the evaluation process. This study focuses in Stain Color Normalization (SCN) within a Whole-Slide Image (WSI) cohort, aiming to reduce batch biases. Building on published graphical method, this research demonstrates a mathematical population or data-driven method that optimizes the dependency on the number of reference WSIs and corresponding aggregate sums, thereby increasing SCN process efficiency. This method expedites the analysis of color convergence 50-fold by using stain vector Euclidean distance analysis, slashing the requirement for reference WSIs by more than half. The approach is validated through a tripartite methodology: 1) Stain vector euclidean distances analysis, 2) Distance computation timing, and 3) Qualitative and quantitative assessments of SCN across cancer tumors regions of interest. The results validate the performance of data-driven SCN method, thus potential to enhance the precision and reliability of computational pathology analyses. This advancement is poised to enhance diagnostic processes, therapeutic strategies, and patient prognosis.
Authors
- Michael D. Feldman (ORCID: https://orcid.org/0000-0002-6661-4940)
- Angela N. Viaene (ORCID: https://orcid.org/0000-0001-6430-8360)
- Tahsin Kurç (ORCID: https://orcid.org/0000-0001-9237-4306)
- Russell Taki Shinohara (ORCID: https://orcid.org/0000-0001-8627-8203)
- Spyridon Bakas (ORCID: https://orcid.org/0000-0001-8734-6482)
- Andrew A. Chen (ORCID: https://orcid.org/0000-0002-5027-6422)
- MacLean P. Nasrallah (ORCID: https://orcid.org/0000-0003-4861-0898)
- Caleb M. Grenko (ORCID: https://orcid.org/0000-0002-3926-5503)
- Parker C. Wilson (ORCID: https://orcid.org/0000-0001-8647-9662)
- Joel Saltz (ORCID: https://orcid.org/0000-0002-3451-2165)
- Hamed Akbari (ORCID: https://orcid.org/0000-0001-9786-3707)
- Parth Sharma (ORCID: https://orcid.org/0000-0002-5701-0619)
- Jose L. Agraz (ORCID: https://orcid.org/0000-0001-6582-2783)
- Carlos Agraz (ORCID: https://orcid.org/0009-0009-4036-2106)
- Sven Aelterman (ORCID: https://orcid.org/0009-0002-1474-7335)
- Amanda Tan
- Robert S. Pozos (ORCID: https://orcid.org/0000-0002-8498-8276)
- Charles L. Rice
Institutions
- Microsoft (United States) (US)
- Children's Hospital of Philadelphia (US)
- Davidson College (US)
- Medical University of South Carolina (US)
- Santa Clara University (US)
- San Diego State University (US)
- Internet2 (US)
- Penn Center for AIDS Research (US)
- Philadelphia Department of Public Health (US)
- Stony Brook University (US)
- University of Pennsylvania (US)
- Philadelphia University (US)
Publication Details
- Journal
- IEEE Open Journal of Engineering in Medicine and Biology
- Published
- 2024-09-06
- DOI
- https://doi.org/10.1109/ojemb.2024.3455011
- Citations
- 1
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- National Science Foundation
- University of Pennsylvania
- National Institutes of Health