# Extracting quantitative biological information from brightfield cell images using deep learning

arXiv: https://arxiv.org/abs/2012.12986 · HTML: https://mlsanity.com/paper/2012.12986/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Artificial Immunofluorescence in a Flash: Rapid Synthetic Imaging from Brightfield Through Residual Diffusion](https://mlsanity.com/paper/2407.17882/) (2024)
- [Improved Sensor-Based Animal Behavior Classification Performance through Conditional Generative Adversarial Network](https://mlsanity.com/paper/2209.03758/) (2022)
- [Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps](https://mlsanity.com/paper/2210.07102/) (2022)

## Abstract (arXiv)

Quantitative analysis of cell structures is essential for biomedical and pharmaceutical research. The standard imaging approach relies on fluorescence microscopy, where cell structures of interest are labeled by chemical staining techniques. However, these techniques are often invasive and sometimes even toxic to the cells, in addition to being time-consuming, labor-intensive, and expensive. Here, we introduce an alternative deep-learning-powered approach based on the analysis of brightfield images by a conditional generative adversarial neural network (cGAN). We show that this approach can extract information from the brightfield images to generate virtually-stained images, which can be used in subsequent downstream quantitative analyses of cell structures. Specifically, we train a cGAN to virtually stain lipid droplets, cytoplasm, and nuclei using brightfield images of human stem-cell-derived fat cells (adipocytes), which are of particular interest for nanomedicine and vaccine development. Subsequently, we use these virtually-stained images to extract quantitative measures about these cell structures. Generating virtually-stained fluorescence images is less invasive, less expensive, and more reproducible than standard chemical staining; furthermore, it frees up the fluorescence microscopy channels for other analytical probes, thus increasing the amount of information that can be extracted from each cell.