_:n6004393b30b04ac5946ecd0c32b7cc5bxb38
<http://schema.org/description>
"Prostate cancer (PCa) is graded by pathologists by examining the\narchitectural pattern of cancerous epithelial tissue on hematoxylin and eosin\n(H&E) stained slides. Given the importance of gland morphology, automatically\ndifferentiating between glandular epithelial tissue and other tissues is an\nimportant prerequisite for the development of automated methods for detecting\nPCa. We propose a new method, using deep learning, for automatically segmenting\nepithelial tissue in digitized prostatectomy slides. We employed\nimmunohistochemistry (IHC) to render the ground truth less subjective and more\nprecise compared to manual outlining on H&E slides, especially in areas with\nhigh-grade and poorly differentiated PCa. Our dataset consisted of 102 tissue\nblocks, including both low and high grade PCa. From each block a single new\nsection was cut, stained with H&E, scanned, restained using P63 and CK8/18 to\nhighlight the epithelial structure, and scanned again. The H&E slides were\nco-registered to the IHC slides. On a subset of the IHC slides we applied color\ndeconvolution, corrected stain errors manually, and trained a U-Net to perform\nsegmentation of epithelial structures. Whole-slide segmentation masks generated\nby the IHC U-Net were used to train a second U-Net on H&E. Our system makes\nprecise cell-level segmentations and segments both intact glands as well as\nindividual (tumor) epithelial cells. We achieved an F1-score of 0.895 on a\nhold-out test set and 0.827 on an external reference set from a different\ncenter. We envision this segmentation as being the first part of a fully\nautomated prostate cancer detection and grading pipeline."
<https://deepai.org/profile/wouter-bulten>