A benchmarked comparison of software packages for time-lapse image processing of monolayer bacterial population dynamics

Time-lapse microscopy offers a powerful approach for analysing cellular activity. In particular, this technique is valuable for assessing the behaviour of bacterial populations, which can exhibit growth and intercellular interactions in monolayer. Such time-lapse imaging typically generates large qu...

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Bibliographic Details
Published inbioRxiv
Main Authors Ahmadi, Atiyeh, Courtney, Matthew, Ren, Carolyn, Ingalls, Brian P
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 03.12.2023
Cold Spring Harbor Laboratory
Edition1.1
Subjects
Image processing
Microbiology
Population dynamics
Software
Software packages
image processing
microbiology
time-lapse imaging
segmentation
tracking
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Summary:Time-lapse microscopy offers a powerful approach for analysing cellular activity. In particular, this technique is valuable for assessing the behaviour of bacterial populations, which can exhibit growth and intercellular interactions in monolayer. Such time-lapse imaging typically generates large quantities of data, limiting the options for manual investigation. Several of image processing software packages have been developed to facilitate analysis. It can thus be a challenge to identify the software package best suited to a particular research goal. Here, we compare four software packages that support analysis of 2D time-lapse images of cellular populations: CellProfiler, SuperSegger-Omnipose, DeLTA, and FAST. We compare their performance against benchmarked results on time-lapse observations of E. coli populations. Performance varies across the packages, with each of the four out-performing the others in at least one aspect of the analysis. Not surprisingly, the packages that have been in development for longer showed the strongest performance. We found that deep-learning based approaches to object segmentation outperformed traditional approaches, but the opposite was true for frame-to-frame object tracking. We offer these comparisons, together with insight on usability, computational efficiency, and feature availability, as a guide to researchers seeking image processing solutions.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://github.com/ingallslab/ImageProcessing-Benchmarking
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
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Competing Interest Statement: The authors have declared no competing interest.
ISSN:2692-8205
2692-8205
DOI:10.1101/2023.11.30.569426