  Gleason grading specified in ISUP 2014 is the clinical standard in staging
prostate cancer and the most important part of the treatment decision. However,
the grading is subjective and suffers from high intra and inter-user
variability. To improve the consistency and objectivity in the grading, we
introduced glandular tissue WithOut Basal cells (WOB) as the ground truth. The
presence of basal cells is the most accepted biomarker for benign glandular
tissue and the absence of basal cells is a strong indicator of acinar prostatic
adenocarcinoma, the most common form of prostate cancer. Glandular tissue can
objectively be assessed as WOB or not WOB by using specific immunostaining for
glandular tissue (Cytokeratin 8/18) and for basal cells (Cytokeratin 5/6 +
p63). Even more, WOB allowed us to develop a semi-automated data generation
pipeline to speed up the tremendously time consuming and expensive process of
annotating whole slide images by pathologists. We generated 295 prostatectomy
images exhaustively annotated with WOB. Then we used our Deep Learning
Framework, which achieved the $2^{nd}$ best reported score in Camelyon17
Challenge, to train networks for segmenting WOB in needle biopsies. Evaluation
of the model on 63 needle biopsies showed promising results which were improved
further by finetuning the model on 118 biopsies annotated with WOB, achieving
F1-score of 0.80 and Precision-Recall AUC of 0.89 at the pixel-level. Then we
compared the performance of the model against 17 biopsies annotated
independently by 3 pathologists using only H\&E staining. The comparison
demonstrated that the model performed on a par with the pathologists. Finally,
the model detected and accurately outlined existing WOB areas in two biopsies
incorrectly annotated as totally WOB-free biopsies by three pathologists and in
one biopsy by two pathologists.
