Journal Article

FZJ-2022-03031

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png CellSium – versatile cell simulator for microcolony ground truth generation

Sachs, C. C. ; Ruzaeva, K.FZJ* ; Seiffarth, J.FZJ* ; Wiechert, W.FZJ* ; Berkels, B. ; Nöh, K. (Corresponding author)FZJ*

2022
Oxford University Press Oxford

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Please use a persistent id in citations: http://hdl.handle.net/2128/31666  doi:10.1093/bioadv/vbac053

Abstract: To train deep learning based segmentation models, large ground truth data sets are needed. To address this need in microfluidic live-cell imaging, we present CellSium, a flexibly configurable cell simulator built to synthesize realistic image sequences of bacterial microcolonies growing in monolayers. We illustrate that the simulated images are suitable for training neural networks. Synthetic time-lapse videos with and without fluorescence, using programmable cell growth models, and simulation-ready 3D colony geometries for computational fluid dynamics (CFD) are also supported.

Note: Funding: - Deutsche Forschungsgemeinschaft [WI 1705/16-2], [491111487]- the President’s Initiative and Networking Funds of the Helmholtz Association of German Research Centres [SATOMI ZT-I-PF-04-011]- Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)

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Contributing Institute(s):

1. Biotechnologie (IBG-1)

Research Program(s):

1. 2172 - Utilization of renewable carbon and energy sources and engineering of ecosystem functions (POF4-217) (POF4-217)

Appears in the scientific report 2022

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