  Microscopy images from different imaging conditions, organs, and tissues
often have numerous cells with various shapes on a range of backgrounds. As a
result, designing a deep learning model to count cells in a source domain
becomes precarious when transferring them to a new target domain. To address
this issue, manual annotation costs are typically the norm when training deep
learning-based cell counting models across different domains. In this paper, we
propose a cross-domain cell counting approach that requires only weak human
annotation efforts. Initially, we implement a cell counting network that
disentangles domain-specific knowledge from domain-agnostic knowledge in cell
images, where they pertain to the creation of domain style images and cell
density maps, respectively. We then devise an image synthesis technique capable
of generating massive synthetic images founded on a few target-domain images
that have been labeled. Finally, we use a public dataset consisting of
synthetic cells as the source domain, where no manual annotation cost is
present, to train our cell counting network; subsequently, we transfer only the
domain-agnostic knowledge to a new target domain of real cell images. By
progressively refining the trained model using synthesized target-domain images
and several real annotated ones, our proposed cross-domain cell counting method
achieves good performance compared to state-of-the-art techniques that rely on
fully annotated training images in the target domain. We evaluated the efficacy
of our cross-domain approach on two target domain datasets of actual microscopy
cells, demonstrating the feasibility of requiring annotations on only a few
images in a new domain.
