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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| Published in | bioRxiv |
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| Main Authors | , , , |
| Format | Paper |
| Language | English |
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Cold Spring Harbor
Cold Spring Harbor Laboratory Press
03.12.2023
Cold Spring Harbor Laboratory |
| Edition | 1.1 |
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| ISSN | 2692-8205 2692-8205 |
| DOI | 10.1101/2023.11.30.569426 |
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| Abstract | 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 |
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| AbstractList | 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.
Time-lapse microscopy provides a detailed window into the world of bacterial behavior. However, the vast amount of data produced by these techniques is difficult to analyze manually. We have analysed four software tools designed to process such data and compared their performance, using populations of commonly studied bacterial species as our test subjects. Our findings offer a roadmap to scientists, helping them choose the right tool for their research. This comparison bridges a gap between microbiology and computational analysis, streamlining research efforts. 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 |
| Author | Ingalls, Brian P Ahmadi, Atiyeh Ren, Carolyn Courtney, Matthew |
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| Copyright | 2023. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (“the License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023, Posted by Cold Spring Harbor Laboratory |
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| DOI | 10.1101/2023.11.30.569426 |
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| Keywords | image processing microbiology time-lapse imaging segmentation tracking |
| Language | English |
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| Title | A benchmarked comparison of software packages for time-lapse image processing of monolayer bacterial population dynamics |
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