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000909145 1001_ $$0P:(DE-Juel1)157870$$aSachs, Christian Carsten$$b0
000909145 245__ $$aCellSium – versatile cell simulator for microcolony ground truth generation
000909145 260__ $$aOxford$$bOxford University Press$$c2022
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000909145 500__ $$aFunding: -	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)
000909145 520__ $$aTo train deep learning based segmentation models, large ground truth data sets are needed. To address this need in microfluidic live-cell imaging, we present CellSium, a flexibly configurable cell simulator built to synthesize realistic image sequences of bacterial microcolonies growing in monolayers. We illustrate that the simulated images are suitable for training neural networks. Synthetic time-lapse videos with and without fluorescence, using programmable cell growth models, and simulation-ready 3D colony geometries for computational fluid dynamics (CFD) are also supported.
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000909145 7001_ $$0P:(DE-Juel1)180323$$aRuzaeva, Karina$$b1
000909145 7001_ $$0P:(DE-Juel1)176923$$aSeiffarth, Johannes$$b2
000909145 7001_ $$0P:(DE-Juel1)129076$$aWiechert, Wolfgang$$b3
000909145 7001_ $$0P:(DE-HGF)0$$aBerkels, Benjamin$$b4
000909145 7001_ $$0P:(DE-Juel1)129051$$aNöh, Katharina$$b5$$eCorresponding author
000909145 773__ $$0PERI:(DE-600)3076075-6$$a10.1093/bioadv/vbac053$$gp. vbac053$$n1$$pvbac053$$tBioinformatics advances$$v2$$x2635-0041$$y2022
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