mlsanityData to 7 Oct 2026

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

arXiv 2106.08285 ↗First posted 15 Jun 2021cs.CV cs.LG eess.IVcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 45 references that are in the map. First posted on arXiv 15 Jun 2021.

Builds on

22 of 45 references.

References · 22

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  5. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  6. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  7. 2016Improved Techniques for Training GANscited by 2,938
  8. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  9. 2017Non-local Neural Networkscited by 2,290
  10. 2020Training Generative Adversarial Networks with Limited Datacited by 1,184
  11. 2018Which Training Methods for GANs do actually Converge?cited by 672
  12. 2019Image2StyleGAN++: How to Edit the Embedded Images?cited by 356
10 more
  1. 2019Kornia: an Open Source Differentiable Computer Vision Library for PyTorchcited by 231
  2. 2018A Note on the Inception Scorecited by 225
  3. 2020A U-Net Based Discriminator for Generative Adversarial Networkscited by 128
  4. 2020Attention-Based Transformers for Instance Segmentation of Cells in Microstructurescited by 38
  5. 2017GANs for Biological Image Synthesiscited by 12
  6. 2019Red blood cell image generation for data augmentation using Conditional Generative Adversarial Networkscited by 12
  7. 2020Multiclass Yeast Segmentation in Microstructured Environments with Deep Learningcited by 7
  8. 2020Learning Generative Models of Tissue Organization with Supervised GANscited by 6
  9. 2021Maximizing Information Gain for the Characterization of Biomolecular Circuitscited by 3
  10. 2017Generative Modeling with Conditional Autoencoders: Building an Integrated Cellcited by 2

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 1.

4 papers.

  1. 2022Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labelscited by 6
  2. 2022Unveiling the Tapestry: the Interplay of Generalization and Forgetting in Continual Learningcited by 3
  3. 2023An Instance Segmentation Dataset of Yeast Cells in Microstructurescited by 3
  4. 2024A Perspective on Deep Vision Performance with Standard Image and Video Codecscited by 1