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Accurate cell counting in microscopic images is important for medicaldiagnoses and biological studies. However, manual cell counting is verytime-consuming, tedious, and prone to subjective errors. We propose a newdensity regression-based method for automatic cell counting that reduces theneed to manually annotate experimental images. A supervised learning-baseddensity regression model (DRM) is trained with annotated synthetic images (thesource domain) and their corresponding ground truth density maps. A domainadaptation model (DAM) is built to map experimental images (the target domain)to the feature space of the source domain. By use of the unsupervisedlearning-based DAM and supervised learning-based DRM, a cell density map of agiven target image can be estimated, from which the number of cells can becounted. Results from experimental immunofluorescent microscopic images ofhuman embryonic stem cells demonstrate the promising performance of theproposed counting method.

Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression