  Accurate cell counting in microscopic images is important for medical
diagnoses and biological studies. However, manual cell counting is very
time-consuming, tedious, and prone to subjective errors. We propose a new
density regression-based method for automatic cell counting that reduces the
need to manually annotate experimental images. A supervised learning-based
density regression model (DRM) is trained with annotated synthetic images (the
source domain) and their corresponding ground truth density maps. A domain
adaptation model (DAM) is built to map experimental images (the target domain)
to the feature space of the source domain. By use of the unsupervised
learning-based DAM and supervised learning-based DRM, a cell density map of a
given target image can be estimated, from which the number of cells can be
counted. Results from experimental immunofluorescent microscopic images of
human embryonic stem cells demonstrate the promising performance of the
proposed counting method.
