Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy

TL;DR

Multi-StyleGAN is a novel generative model that simulates realistic time-lapse fluorescence microscopy images of living cells, capturing biophysical dynamics and aiding experimental and analytical advancements.

Contribution

It introduces Multi-StyleGAN, a new generative adversarial network capable of synthesizing multi-domain, time-dependent cellular imagery based on real experimental data.

Findings

Successfully simulates cell morphology, growth, and interactions.

Captures fluorescence intensity variations over time.

Provides a tool for generating training data for cell analysis.

Abstract

Time-lapse fluorescent microscopy (TLFM) combined with predictive mathematical modelling is a powerful tool to study the inherently dynamic processes of life on the single-cell level. Such experiments are costly, complex and labour intensive. A complimentary approach and a step towards in silico experimentation, is to synthesise the imagery itself. Here, we propose Multi-StyleGAN as a descriptive approach to simulate time-lapse fluorescence microscopy imagery of living cells, based on a past experiment. This novel generative adversarial network synthesises a multi-domain sequence of consecutive timesteps. We showcase Multi-StyleGAN on imagery of multiple live yeast cells in microstructured environments and train on a dataset recorded in our laboratory. The simulation captures underlying biophysical factors and time dependencies, such as cell morphology, growth, physical interactions, as well as the intensity of a fluorescent reporter protein. An immediate application is to generate additional training and validation data for feature extraction algorithms or to aid and expedite development of advanced experimental techniques such as online monitoring or control of cells. Code and dataset is available at https://git.rwth-aachen.de/bcs/projects/tp/multi-stylegan.