  Cell imaging assays utilizing fluorescence stains are essential for observing
sub-cellular organelles and their responses to perturbations. Immunofluorescent
staining process is routinely in labs, however the recent innovations in
generative AI is challenging the idea of IF staining are required. This is
especially true when the availability and cost of specific fluorescence dyes is
a problem to some labs. Furthermore, staining process takes time and leads to
inter-intra technician and hinders downstream image and data analysis, and the
reusability of image data for other projects. Recent studies showed the use of
generated synthetic immunofluorescence (IF) images from brightfield (BF) images
using generative AI algorithms in the literature. Therefore, in this study, we
benchmark and compare five models from three types of IF generation backbones,
CNN, GAN, and diffusion models, using a publicly available dataset. This paper
not only serves as a comparative study to determine the best-performing model
but also proposes a comprehensive analysis pipeline for evaluating the efficacy
of generators in IF image synthesis. We highlighted the potential of deep
learning-based generators for IF image synthesis, while also discussed
potential issues and future research directions. Although generative AI shows
promise in simplifying cell phenotyping using only BF images with IF staining,
further research and validations are needed to address the key challenges of
model generalisability, batch effects, feature relevance and computational
costs.
