TY  - JOUR
AU  - Sachs, Christian Carsten
AU  - Ruzaeva, Karina
AU  - Seiffarth, Johannes
AU  - Wiechert, Wolfgang
AU  - Berkels, Benjamin
AU  - Nöh, Katharina
TI  - CellSium – versatile cell simulator for microcolony ground truth generation
JO  - Bioinformatics advances
VL  - 2
IS  - 1
SN  - 2635-0041
CY  - Oxford
PB  - Oxford University Press
M1  - FZJ-2022-03031
SP  - vbac053
PY  - 2022
N1  - 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)
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 36699390
UR  - <Go to ISI:>//WOS:001153137500029
DO  - DOI:10.1093/bioadv/vbac053
UR  - https://juser.fz-juelich.de/record/909145
ER  -