  Object counting and localization are key steps for quantitative analysis in
large-scale microscopy applications. This procedure becomes challenging when
target objects are overlapping, are densely clustered, and/or present fuzzy
boundaries. Previous methods producing density maps based on deep learning have
reached a high level of accuracy for object counting by assuming that object
counting is equivalent to the integration of the density map. However, this
model fails when objects show significant overlap regarding accurate
localization. We propose an alternative method to count and localize objects
from the density map to overcome this limitation. Our procedure includes the
following three key aspects: 1) Proposing a new counting method based on the
statistical properties of the density map, 2) optimizing the counting results
for those objects which are well-detected based on the proposed counting
method, and 3) improving localization of poorly detected objects using the
proposed counting method as prior information. Validation includes processing
of microscopy data with known ground truth and comparison with other models
that use conventional processing of the density map. Our results show improved
performance in counting and localization of objects in 2D and 3D microscopy
data. Furthermore, the proposed method is generic, considering various
applications that rely on the density map approach. Our code will be released
post-review.

Computer Vision and Pattern Recognition;Machine Learning