CellSium: versatile cell simulator for microcolony ground truth generation.

ABSTRACT:

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.6193

SUBMITTER: Sachs CC 

PROVIDER: S-EPMC9710621 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature