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Sci Rep. 2019 Jan 29;9(1):864. doi: 10.1038/s41598-018-37257-4.

Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard.

Author information

1
Radboud University Medical Center, Diagnostic Image Analysis Group and the Department of Pathology, 6500HB, Nijmegen, The Netherlands. wouter.bulten@radboudumc.nl.
2
Radboud University Medical Center, Diagnostic Image Analysis Group and the Department of Pathology, 6500HB, Nijmegen, The Netherlands.
3
Radboud University Medical Center, Department of Pathology, 6500HB, Nijmegen, The Netherlands.
4
Fraunhofer MEVIS, 23562, Lübeck, Germany.
5
Radboud University Medical Center, Diagnostic Image Analysis Group and the Department of Radiology and Nuclear Medicine, 6500HB, Nijmegen, The Netherlands.

Abstract

Given the importance of gland morphology in grading prostate cancer (PCa), automatically differentiating between epithelium and other tissues is an important prerequisite for the development of automated methods for detecting PCa. We propose a new deep learning method to segment epithelial tissue in digitised hematoxylin and eosin (H&E) stained prostatectomy slides using immunohistochemistry (IHC) as reference standard. We used IHC to create a precise and objective ground truth compared to manual outlining on H&E slides, especially in areas with high-grade PCa. 102 tissue sections were stained with H&E and subsequently restained with P63 and CK8/18 IHC markers to highlight epithelial structures. Afterwards each pair was co-registered. First, we trained a U-Net to segment epithelial structures in IHC using a subset of the IHC slides that were preprocessed with color deconvolution. Second, this network was applied to the remaining slides to create the reference standard used to train a second U-Net on H&E. Our system accurately segmented both intact glands and individual tumour epithelial cells. The generalisation capacity of our system is shown using an independent external dataset from a different centre. We envision this segmentation as the first part of a fully automated prostate cancer grading pipeline.

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