Citation

BibTex format

@article{Xing:2024:10.1016/j.compbiomed.2024.109102,
author = {Xing, X and Murdoch, S and Tang, C and Papanastasiou, G and Cross-Zamirski, J and Guo, Y and Xiao, X and Schönlieb, C-B and Wang, Y and Yang, G},
doi = {10.1016/j.compbiomed.2024.109102},
journal = {Comput Biol Med},
title = {Can generative AI replace immunofluorescent staining processes? A comparison study of synthetically generated cellpainting images from brightfield.},
url = {http://dx.doi.org/10.1016/j.compbiomed.2024.109102},
volume = {182},
year = {2024}
}
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TY  - JOUR
AB  - Cell imaging assays utilising 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 wet lab immunofluorescence (IF) staining. 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 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.
AU  - Xing,X
AU  - Murdoch,S
AU  - Tang,C
AU  - Papanastasiou,G
AU  - Cross-Zamirski,J
AU  - Guo,Y
AU  - Xiao,X
AU  - Schönlieb,C-B
AU  - Wang,Y
AU  - Yang,G
DO  - 10.1016/j.compbiomed.2024.109102
PY  - 2024///
TI  - Can generative AI replace immunofluorescent staining processes? A comparison study of synthetically generated cellpainting images from brightfield.
T2  - Comput Biol Med
UR  - http://dx.doi.org/10.1016/j.compbiomed.2024.109102
UR  - https://www.ncbi.nlm.nih.gov/pubmed/39255659
VL  - 182
ER  -
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