  Cell imaging and analysis are fundamental to biomedical research because
cells are the basic functional units of life. Among different cell-related
analysis, cell counting and detection are widely used. In this paper, we focus
on one common step of learning-based cell counting approaches: coding the raw
dot labels into more suitable maps for learning. Two criteria of coding raw dot
labels are discussed, and a new coding scheme is proposed in this paper. The
two criteria measure how easy it is to train the model with a coding scheme,
and how robust the recovered raw dot labels are when predicting. The most
compelling advantage of the proposed coding scheme is the ability to
distinguish neighboring cells in crowded regions. Cell counting and detection
experiments are conducted for five coding schemes on four types of cells and
two network architectures. The proposed coding scheme improves the counting
accuracy versus the widely-used Gaussian and rectangle kernels up to 12%, and
also improves the detection accuracy versus the common proximity coding up to
14%.
