  Digital pathology enables automatic analysis of histopathological sections
using artificial intelligence (AI). Automatic evaluation could improve
diagnostic efficiency and help find associations between morphological features
and clinical outcome. For development of such prediction models, identifying
invasive epithelial cells, and separating these from benign epithelial cells
and in situ lesions would be the first step. In this study, we aimed to develop
an AI model for segmentation of epithelial cells in sections from breast
cancer. We generated epithelial ground truth masks by restaining hematoxylin
and eosin (HE) sections with cytokeratin (CK) AE1/AE3, and by pathologists'
annotations. HE/CK image pairs were used to train a convolutional neural
network, and data augmentation was used to make the model more robust. Tissue
microarrays (TMAs) from 839 patients, and whole slide images from two patients
were used for training and evaluation of the models. The sections were derived
from four cohorts of breast cancer patients. TMAs from 21 patients from a fifth
cohort was used as a second test set. In quantitative evaluation, a mean Dice
score of 0.70, 0.79, and 0.75 for invasive epithelial cells, benign epithelial
cells, and in situ lesions, respectively, were achieved. In qualitative scoring
(0-5) by pathologists, results were best for all epithelium and invasive
epithelium, with scores of 4.7 and 4.4. Scores for benign epithelium and in
situ lesions were 3.7 and 2.0. The proposed model segmented epithelial cells in
HE stained breast cancer slides well, but further work is needed for accurate
division between the classes. Immunohistochemistry, together with pathologists'
annotations, enabled the creation of accurate ground truths. The model is made
freely available in FastPathology and the code is available at
https://github.com/AICAN-Research/breast-epithelium-segmentation
