Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy
This work addresses the need for in silico experimentation in cell biology by generating synthetic training data, though it is incremental as it builds on existing StyleGAN methods.
The authors tackled the problem of costly and complex time-lapse fluorescent microscopy experiments by proposing Multi-StyleGAN, a generative adversarial network that synthesizes multi-domain sequences of consecutive timesteps to simulate imagery of living cells, capturing biophysical factors like cell morphology and growth.
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.