  Tissue segmentation is a routine preprocessing step to reduce the
computational cost of whole slide image (WSI) analysis by excluding background
regions. Traditional image processing techniques are commonly used for tissue
segmentation, but often require manual adjustments to parameter values for
atypical cases, fail to exclude all slide and scanning artifacts from the
background, and are unable to segment adipose tissue. Pen marking artifacts in
particular can be a potential source of bias for subsequent analyses if not
removed. In addition, several applications require the separation of individual
cross-sections, which can be challenging due to tissue fragmentation and
adjacent positioning. To address these problems, we develop a convolutional
neural network for tissue and pen marking segmentation using a dataset of 200
H&E stained WSIs. For separating tissue cross-sections, we propose a novel
post-processing method based on clustering predicted centroid locations of the
cross-sections in a 2D histogram. On an independent test set, the model
achieved a mean Dice score of 0.981$\pm$0.033 for tissue segmentation and a
mean Dice score of 0.912$\pm$0.090 for pen marking segmentation. The mean
absolute difference between the number of annotated and separated
cross-sections was 0.075$\pm$0.350. Our results demonstrate that the proposed
model can accurately segment H&E stained tissue cross-sections and pen markings
in WSIs while being robust to many common slide and scanning artifacts. The
model with trained model parameters and post-processing method are made
publicly available as a Python package called SlideSegmenter.
