# CellCycleGAN: Spatiotemporal Microscopy Image Synthesis of Cell Populations using Statistical Shape Models and Conditional GANs

arXiv: https://arxiv.org/abs/2010.12011 · HTML: https://mlsanity.com/paper/2010.12011/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets](https://mlsanity.com/paper/2301.10227/) (2023)
- [Implicit Neural Representations for Generative Modeling of Living Cell Shapes](https://mlsanity.com/paper/2207.06283/) (2022)
- [3D fluorescence microscopy data synthesis for segmentation and benchmarking](https://mlsanity.com/paper/2107.10180/) (2021)
- [Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow Fields](https://mlsanity.com/paper/2403.17808/) (2024)
- [SynCellFactory: Generative Data Augmentation for Cell Tracking](https://mlsanity.com/paper/2404.16421/) (2024)
- [CellStyle: Improved Zero-Shot Cell Segmentation via Style Transfer](https://mlsanity.com/paper/2503.08603/) (2025)
- [Cell Phantom Video Generation in Elliptical Fourier Descriptor Domain](https://mlsanity.com/paper/2605.22563/) (2026)

## Abstract (arXiv)

Automatic analysis of spatio-temporal microscopy images is inevitable for state-of-the-art research in the life sciences. Recent developments in deep learning provide powerful tools for automatic analyses of such image data, but heavily depend on the amount and quality of provided training data to perform well. To this end, we developed a new method for realistic generation of synthetic 2D+t microscopy image data of fluorescently labeled cellular nuclei. The method combines spatiotemporal statistical shape models of different cell cycle stages with a conditional GAN to generate time series of cell populations and provides instance-level control of cell cycle stage and the fluorescence intensity of generated cells. We show the effect of the GAN conditioning and create a set of synthetic images that can be readily used for training and benchmarking of cell segmentation and tracking approaches.