  Due to the recent advancements in machine vision, digital pathology has
gained significant attention. Histopathology images are distinctly rich in
visual information. The tissue glass slide images are utilized for disease
diagnosis. Researchers study many methods to process histopathology images and
facilitate fast and reliable diagnosis; therefore, the availability of
high-quality slides becomes paramount. The quality of the images can be
negatively affected when the glass slides are ink-marked by pathologists to
delineate regions of interest. As an example, in one of the largest public
histopathology datasets, The Cancer Genome Atlas (TCGA), approximately $12\%$
of the digitized slides are affected by manual delineations through ink
markings. To process these open-access slide images and other repositories for
the design and validation of new methods, an algorithm to detect the marked
regions of the images is essential to avoid confusing tissue pixels with
ink-colored pixels for computer methods. In this study, we propose to segment
the ink-marked areas of pathology patches through a deep network. A dataset
from $79$ whole slide images with $4,305$ patches was created and different
networks were trained. Finally, the results showed an FPN model with the
EffiecentNet-B3 as the backbone was found to be the superior configuration with
an F1 score of $94.53\%$.
