  Counting cells in fluorescent microscopy is a tedious, time-consuming task
that researchers have to accomplish to assess the effects of different
experimental conditions on biological structures of interest. Although such
objects are generally easy to identify, the process of manually annotating
cells is sometimes subject to arbitrariness due to the operator's
interpretation of the borderline cases.
  We propose a Machine Learning approach that exploits a fully-convolutional
network in a binary segmentation fashion to localize the objects of interest.
Counts are then retrieved as the number of detected items.
  Specifically, we adopt a UNet-like architecture leveraging residual units and
an extended bottleneck for enlarging the field-of-view. In addition, we make
use of weighted maps that penalize the errors on cells boundaries increasingly
with overcrowding. These changes provide more context and force the model to
focus on relevant features during pixel-wise classification. As a result, the
model performance is enhanced, especially in presence of clumping cells,
artifacts and confounding biological structures. Posterior assessment of the
results with domain experts confirms that the model detects cells of interest
correctly. The model demonstrates a human-level ability inasmuch even erroneous
predictions seem to fall within the limits of operator interpretation. This
qualitative assessment is also corroborated by quantitative metrics as an
${F_1}$ score of 0.87.
  Despite some difficulties in interpretation, results are also satisfactory
with respect to the counting task, as testified by mean and median absolute
error of, respectively, 0.8 and 1.
