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

Prangemeier, T. ; Reich, C. ; Wildner, C. ; Koeppl, H. (2021):
Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy.
In: LNCS, 12908, In: Medical Image Computing and Computer Assisted Intervention - MICCAI 2021, pp. 476-486,
Springer, 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, France, 27.09.-01.10.2021, ISBN 978-3-030-87236-6,
DOI: 10.1007/978-3-030-87237-3_46,
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2021
Creators: Prangemeier, T. ; Reich, C. ; Wildner, C. ; Koeppl, H.
Title: Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy
Language: English
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.

Book Title: Medical Image Computing and Computer Assisted Intervention - MICCAI 2021
Series: LNCS
Series Volume: 12908
Publisher: Springer
ISBN: 978-3-030-87236-6
Uncontrolled Keywords: Computer Vision and Pattern Recognition, Machine Learning, Image and Video Processing, Quantitative Methods
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
Event Title: 24th International Conference on Medical Image Computing and Computer-Assisted Intervention
Event Location: Strasbourg, France
Event Dates: 27.09.-01.10.2021
Date Deposited: 06 Sep 2021 07:03
DOI: 10.1007/978-3-030-87237-3_46
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