  Cell counting is a ubiquitous, yet tedious task that would greatly benefit
from automation. From basic biological questions to clinical trials, cell
counts provide key quantitative feedback that drive research. Unfortunately,
cell counting is most commonly a manual task and can be time-intensive. The
task is made even more difficult due to overlapping cells, existence of
multiple focal planes, and poor imaging quality, among other factors. Here, we
describe a convolutional neural network approach, using a recently described
feature pyramid network combined with a VGG-style neural network, for
segmenting and subsequent counting of cells in a given microscopy image.
