%0 Journal Article
%A Sachs, Christian Carsten
%A Ruzaeva, Karina
%A Seiffarth, Johannes
%A Wiechert, Wolfgang
%A Berkels, Benjamin
%A Nöh, Katharina
%T CellSium – versatile cell simulator for microcolony ground truth generation
%J Bioinformatics advances
%V 2
%N 1
%@ 2635-0041
%C Oxford
%I Oxford University Press
%M FZJ-2022-03031
%P vbac053
%D 2022
%Z 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)
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ 36699390
%U <Go to ISI:>//WOS:001153137500029
%R 10.1093/bioadv/vbac053
%U https://juser.fz-juelich.de/record/909145