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Automated annotations of epithelial cells and stroma in hematoxylin-eosin-stained whole-slide images using cytokeratin re-staining
T. Brázdil, M. Gallo, R. Nenutil, A. Kubanda, M. Toufar, P. Holub
Jazyk angličtina Země Velká Británie
Typ dokumentu časopisecké články, práce podpořená grantem
NLK
Directory of Open Access Journals
od 2015
PubMed Central
od 2015
Europe PubMed Central
od 2015
ProQuest Central
od 2015-01-01
Open Access Digital Library
od 2015-01-01
Health & Medicine (ProQuest)
od 2015-01-01
Wiley-Blackwell Open Access Titles
od 2015
ROAD: Directory of Open Access Scholarly Resources
od 2015
PubMed
34716754
DOI
10.1002/cjp2.249
Knihovny.cz E-zdroje
- MeSH
- barvení a značení MeSH
- deep learning * MeSH
- eosin MeSH
- epitelové buňky MeSH
- hematoxylin MeSH
- keratiny * MeSH
- lidé MeSH
- umělá inteligence MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The diagnosis of solid tumors of epithelial origin (carcinomas) represents a major part of the workload in clinical histopathology. Carcinomas consist of malignant epithelial cells arranged in more or less cohesive clusters of variable size and shape, together with stromal cells, extracellular matrix, and blood vessels. Distinguishing stroma from epithelium is a critical component of artificial intelligence (AI) methods developed to detect and analyze carcinomas. In this paper, we propose a novel automated workflow that enables large-scale guidance of AI methods to identify the epithelial component. The workflow is based on re-staining existing hematoxylin and eosin (H&E) formalin-fixed paraffin-embedded sections by immunohistochemistry for cytokeratins, cytoskeletal components specific to epithelial cells. Compared to existing methods, clinically available H&E sections are reused and no additional material, such as consecutive slides, is needed. We developed a simple and reliable method for automatic alignment to generate masks denoting cytokeratin-rich regions, using cell nuclei positions that are visible in both the original and the re-stained slide. The registration method has been compared to state-of-the-art methods for alignment of consecutive slides and shows that, despite being simpler, it provides similar accuracy and is more robust. We also demonstrate how the automatically generated masks can be used to train modern AI image segmentation based on U-Net, resulting in reliable detection of epithelial regions in previously unseen H&E slides. Through training on real-world material available in clinical laboratories, this approach therefore has widespread applications toward achieving AI-assisted tumor assessment directly from scanned H&E sections. In addition, the re-staining method will facilitate additional automated quantitative studies of tumor cell and stromal cell phenotypes.
Department of Pathology Masaryk Memorial Cancer Institute Brno Czech Republic
Faculty of Informatics Masaryk University Brno Czech Republic
Institute of Computer Science Masaryk University Brno Czech Republic
Citace poskytuje Crossref.org
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