  Immunofluorescent (IF) imaging is crucial for visualizing biomarker
expressions, cell morphology and assessing the effects of drug treatments on
sub-cellular components. IF imaging needs extra staining process and often
requiring cell fixation, therefore it may also introduce artefects and alter
endogenouous cell morphology. Some IF stains are expensive or not readily
available hence hindering experiments. Recent diffusion models, which
synthesise high-fidelity IF images from easy-to-acquire brightfield (BF)
images, offer a promising solution but are hindered by training instability and
slow inference times due to the noise diffusion process. This paper presents a
novel method for the conditional synthesis of IF images directly from BF images
along with cell segmentation masks. Our approach employs a Residual Diffusion
process that enhances stability and significantly reduces inference time. We
performed a critical evaluation against other image-to-image synthesis models,
including UNets, GANs, and advanced diffusion models. Our model demonstrates
significant improvements in image quality (p<0.05 in MSE, PSNR, and SSIM),
inference speed (26 times faster than competing diffusion models), and accurate
segmentation results for both nuclei and cell bodies (0.77 and 0.63 mean IOU
for nuclei and cell true positives, respectively). This paper is a substantial
advancement in the field, providing robust and efficient tools for cell image
analysis.
