Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell
Microscopy
- URL: http://arxiv.org/abs/2106.08285v1
- Date: Tue, 15 Jun 2021 16:51:16 GMT
- Title: Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell
Microscopy
- Authors: Tim Prangemeier, Christoph Reich, Christian Wildner and Heinz Koeppl
- Abstract summary: We propose Multi-StyleGAN as a descriptive approach to simulate time-lapse fluorescence microscopy imagery of living cells.
This novel generative adversarial network synthesises a multi-domain sequence of consecutive timesteps.
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.
- Score: 23.720106678247888
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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
completely in silico experiments, 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.
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