Automated annotations of epithelial cells and stroma in hematoxylin–eosin-stained whole-slide images using cytokeratin re-staining
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Periodical |
| Magazine / Source | The Journal of Pathology: Clinical Research |
| MU Faculty or unit | |
| Citation | |
| web | Wiley Online |
| Doi | http://dx.doi.org/10.1002/cjp2.249 |
| Keywords | U-Net; artificial intelligence; digital pathology; H&E; immunohistochemistry; deep learning; tissue registration |
| Description | 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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