  $\bf{Purpose:}$ The goal of this study was (i) to use artificial intelligence
to automate the traditionally labor-intensive process of manual segmentation of
tumor regions in pathology slides performed by a pathologist and (ii) to
validate the use of a well-known and readily available deep learning
architecture. Automation will reduce the human error involved in manual
delineation, increase efficiency, and result in accurate and reproducible
segmentation. This advancement will alleviate the bottleneck in the workflow in
clinical and research applications due to a lack of pathologist time. Our
application is patient-specific microdosimetry and radiobiological modeling,
which builds on the contoured pathology slides.
  $\bf{Methods:}$ A U-Net architecture was used to segment tumor regions in
pathology core biopsies of lung tissue with adenocarcinoma stained using
hematoxylin and eosin. A pathologist manually contoured the tumor regions in 56
images with binary masks for training. Overlapping patch extraction with
various patch sizes and image downsampling were investigated individually. Data
augmentation and 8-fold cross-validation were used.
  $\bf{Results:}$ The U-Net achieved accuracy of 0.91$\pm$0.06, specificity of
0.90$\pm$0.08, sensitivity of 0.92$\pm$0.07, and precision of 0.8$\pm$0.1. The
F1/DICE score was 0.85$\pm$0.07, with a segmentation time of 3.24$\pm$0.03
seconds per image, achieving a 370$\pm$3 times increased efficiency over manual
segmentation. In some cases, the U-Net correctly delineated the tumor's stroma
from its epithelial component in regions that were classified as tumor by the
pathologist.
  $\bf{Conclusion:}$ The U-Net architecture can segment images with a level of
efficiency and accuracy that makes it suitable for tumor segmentation of
histopathological images in fields such as radiotherapy dosimetry, specifically
in the subfields of microdosimetry.
