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

## TL;DR

This paper introduces an automatic cell counting method using unsupervised adversarial domain adaptation and supervised density regression, reducing manual annotation and improving counting accuracy in microscopic images.

## Contribution

It presents a novel combination of unsupervised domain adaptation and supervised density regression for accurate, automatic cell counting in microscopy images.

## Key findings

- Effective in counting cells in immunofluorescent images
- Reduces need for manual annotation
- Demonstrates promising performance on real data

## Abstract

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.