  Most tumor grading systems for human as for veterinary histopathology are
based upon the absolute count of mitotic figures in a certain reference area of
a histology slide. Since time for prognostication is limited in a diagnostic
setting, the pathologist will often almost arbitrarily choose a certain field
of interest assumed to have the highest mitotic activity. However, as mitotic
figures are commonly very sparse on the slide and often have a patchy
distribution, this poses a sampling problem which is known to be able to
influence the tumor prognostication. On the other hand, automatic detection of
mitotic figures can't yet be considered reliable enough for clinical
application. In order to aid the work of the human expert and at the same time
reduce variance in tumor grading, it is beneficial to assess the whole slide
image (WSI) for the highest mitotic activity and use this as a reference region
for human counting. For this task, we compare two methods for region of
interest proposal, both based on convolutional neural networks (CNN). For both
approaches, the CNN performs a segmentation of the WSI to assess mitotic
activity. The first method performs a segmentation at the original image
resolution, while the second approach performs a segmentation operation at a
significantly reduced resolution, cutting down on processing complexity. We
evaluate the approach using a dataset of 32 completely annotated whole slide
images of canine mast cell tumors, where 22 were used for training of the
network and 10 for test. Our results indicate that, while the overall
correlation to the ground truth mitotic activity is considerably higher (0.94
vs. 0.83) for the approach based upon the fine resolution network, the field of
interest choices are only marginally better. Both approaches propose fields of
interest that contain a mitotic count in the upper quartile of respective
slides.
