Automated annotations of epithelial cells and stroma in hematoxylin–eosin-stained whole-slide images using cytokeratin re-staining

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BRÁZDIL Tomáš GALLO Matej NENUTIL Rudolf KUBANDA Andrej MARTIN Toufar HOLUB Petr

Rok publikování 2021
Druh Článek v odborném periodiku
Časopis / Zdroj The journal of pathology. Clinical research
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www https://onlinelibrary.wiley.com/doi/10.1002/cjp2.249
Doi http://dx.doi.org/10.1002/cjp2.249
Klíčová slova U-Net; artificial intelligence; digital pathology; H&E; immunohistochemistry; deep learning; tissue registration
Popis The diagnosis of solid tumors of epithelial origin (carcinomas) represents a major part of the workload in clinical histopathology. 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 (FFPE) slides by immunohistochemistry for cytokeratins - cytoskeleton components specific to epithelial cells. 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.
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