% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@ARTICLE{Sachs:909145,
author = {Sachs, Christian Carsten and Ruzaeva, Karina and Seiffarth,
Johannes and Wiechert, Wolfgang and Berkels, Benjamin and
Nöh, Katharina},
title = {{C}ell{S}ium – versatile cell simulator for microcolony
ground truth generation},
journal = {Bioinformatics advances},
volume = {2},
number = {1},
issn = {2635-0041},
address = {Oxford},
publisher = {Oxford University Press},
reportid = {FZJ-2022-03031},
pages = {vbac053},
year = {2022},
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)},
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.},
cin = {IBG-1},
ddc = {004},
cid = {I:(DE-Juel1)IBG-1-20101118},
pnm = {2172 - Utilization of renewable carbon and energy sources
and engineering of ecosystem functions (POF4-217)},
pid = {G:(DE-HGF)POF4-2172},
typ = {PUB:(DE-HGF)16},
pubmed = {36699390},
UT = {WOS:001153137500029},
doi = {10.1093/bioadv/vbac053},
url = {https://juser.fz-juelich.de/record/909145},
}