{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-stylegan-towards-image-based-simulation","title":"Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy","arxiv_id":"2106.08285","date":"2021-06-15","proceeding":null,"authors":["Christoph Reich","Tim Prangemeier","Christian Wildner","Heinz Koeppl"],"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.","url_abs":"https://arxiv.org/abs/2106.08285v3","url_pdf":"https://arxiv.org/pdf/2106.08285v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-stylegan-towards-image-based-simulation","repo_url":"https://github.com/ChristophReich1996/Multi-StyleGAN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"descriptive","task_name":"Descriptive"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"tflm-sequence-generation","task_name":"TFLM sequence generation"},{"task_slug":"video-generation","task_name":"Video Generation"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"r1-regularization","method_name":"R1 Regularization"}],"datasets_introduced":[{"slug":"tlfm-dataset","name":"TLFM dataset","full_name":"TLFM dataset for microscopy image sequence generation"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}