Prangemeier, T. ; Reich, C. ; Wildner, C. ; Koeppl, H. (2021)
Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy.
24th International Conference on Medical Image Computing and Computer-Assisted Intervention. Strasbourg, France (27.09.-01.10.2021)
doi: 10.1007/978-3-030-87237-3_46
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Prangemeier, T. ; Reich, C. ; Wildner, C. ; Koeppl, H. |
| Art des Eintrags: | Bibliographie |
| Titel: | Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy |
| Sprache: | Englisch |
| Publikationsjahr: | 21 September 2021 |
| Verlag: | Springer |
| Buchtitel: | Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 |
| Reihe: | LNCS |
| Band einer Reihe: | 12908 |
| Veranstaltungstitel: | 24th International Conference on Medical Image Computing and Computer-Assisted Intervention |
| Veranstaltungsort: | Strasbourg, France |
| Veranstaltungsdatum: | 27.09.-01.10.2021 |
| DOI: | 10.1007/978-3-030-87237-3_46 |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Computer Vision and Pattern Recognition, Machine Learning, Image and Video Processing, Quantitative Methods |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik |
| Hinterlegungsdatum: | 06 Sep 2021 07:03 |
| Letzte Änderung: | 14 Okt 2021 08:06 |
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