In Bioinformatics advances
SUMMARY : To train deep learning-based segmentation models, large ground truth datasets 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 are also supported.
AVAILABILITY AND IMPLEMENTATION : CellSium is free and open source software under the BSD license, implemented in Python, available at github.com/modsim/cellsium (DOI: 10.5281/zenodo.6193033), along with documentation, usage examples and Docker images.
SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics Advances online.
Sachs Christian Carsten, Ruzaeva Karina, Seiffarth Johannes, Wiechert Wolfgang, Berkels Benjamin, Nöh Katharina
2022
