  Prostate cancer (PCa) is graded by pathologists by examining the
architectural pattern of cancerous epithelial tissue on hematoxylin and eosin
(H&E) stained slides. Given the importance of gland morphology, automatically
differentiating between glandular epithelial tissue and other tissues is an
important prerequisite for the development of automated methods for detecting
PCa. We propose a new method, using deep learning, for automatically segmenting
epithelial tissue in digitized prostatectomy slides. We employed
immunohistochemistry (IHC) to render the ground truth less subjective and more
precise compared to manual outlining on H&E slides, especially in areas with
high-grade and poorly differentiated PCa. Our dataset consisted of 102 tissue
blocks, including both low and high grade PCa. From each block a single new
section was cut, stained with H&E, scanned, restained using P63 and CK8/18 to
highlight the epithelial structure, and scanned again. The H&E slides were
co-registered to the IHC slides. On a subset of the IHC slides we applied color
deconvolution, corrected stain errors manually, and trained a U-Net to perform
segmentation of epithelial structures. Whole-slide segmentation masks generated
by the IHC U-Net were used to train a second U-Net on H&E. Our system makes
precise cell-level segmentations and segments both intact glands as well as
individual (tumor) epithelial cells. We achieved an F1-score of 0.895 on a
hold-out test set and 0.827 on an external reference set from a different
center. We envision this segmentation as being the first part of a fully
automated prostate cancer detection and grading pipeline.
