  Manual count of mitotic figures, which is determined in the tumor region with
the highest mitotic activity, is a key parameter of most tumor grading schemes.
It can be, however, strongly dependent on the area selection due to uneven
mitotic figure distribution in the tumor section.We aimed to assess the
question, how significantly the area selection could impact the mitotic count,
which has a known high inter-rater disagreement. On a data set of 32 whole
slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated
for mitotic figures, we asked eight veterinary pathologists (five
board-certified, three in training) to select a field of interest for the
mitotic count. To assess the potential difference on the mitotic count, we
compared the mitotic count of the selected regions to the overall distribution
on the slide.Additionally, we evaluated three deep learning-based methods for
the assessment of highest mitotic density: In one approach, the model would
directly try to predict the mitotic count for the presented image patches as a
regression task. The second method aims at deriving a segmentation mask for
mitotic figures, which is then used to obtain a mitotic density. Finally, we
evaluated a two-stage object-detection pipeline based on state-of-the-art
architectures to identify individual mitotic figures. We found that the
predictions by all models were, on average, better than those of the experts.
The two-stage object detector performed best and outperformed most of the human
pathologists on the majority of tumor cases. The correlation between the
predicted and the ground truth mitotic count was also best for this approach
(0.963 to 0.979). Further, we found considerable differences in position
selection between pathologists, which could partially explain the high variance
that has been reported for the manual mitotic count.
