PDF(Pubmed)
Abstract：
Time-lapse microscopy offers a powerful approach for analyzing cellular activity. In particular, this technique is valuable for assessing the behavior of bacterial populations, which can exhibit growth and intercellular interactions in a monolayer. Such time-lapse imaging typically generates large quantities of data, limiting the options for manual investigation. Several 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 the 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 Escherichia coli populations. Performance varies across the packages, with each of the four outperforming 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 into usability, computational efficiency, and feature availability, as a guide to researchers seeking image-processing solutions.
OBJECTIVE: 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 analyzed 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.
OBJECTIVE: 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 analyzed 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.
摘要：
延时显微镜为分析细胞活动提供了强大的方法。特别是,这种技术对于评估细菌种群的行为很有价值，可以在单层中表现出生长和细胞间相互作用。这种延时成像通常会产生大量的数据，限制手动调查的选项。为了便于分析，已经开发了几个图像处理软件包。因此，确定最适合特定研究目标的软件包可能是一个挑战。这里,我们比较了四个支持分析细胞群2D延时图像的软件包：CellProfiler，SuperSegger-Omnipose,DeLTA,和快。我们将它们的性能与大肠杆菌种群延时观察的基准结果进行了比较。各个软件包的性能各不相同，四个中的每一个在分析的至少一个方面都优于其他。毫不奇怪,开发时间较长的软件包表现出最强的性能。我们发现，基于深度学习的对象分割方法优于传统方法，但帧到帧对象跟踪的情况正好相反。我们提供这些比较，以及对可用性的洞察，计算效率，和功能可用性，作为研究人员寻求图像处理解决方案的指南。
目的：延时显微镜为细菌行为世界提供了一个详细的窗口。然而,这些技术产生的大量数据很难手动分析。我们分析了四种用于处理此类数据的软件工具，并比较了它们的性能，使用通常研究的细菌物种作为我们的测试对象。我们的发现为科学家提供了路线图，帮助他们为他们的研究选择合适的工具。这种比较弥合了微生物学和计算分析之间的差距，精简研究工作。
目的：延时显微镜为细菌行为世界提供了一个详细的窗口。然而,这些技术产生的大量数据很难手动分析。我们分析了四种用于处理此类数据的软件工具，并比较了它们的性能，使用通常研究的细菌物种作为我们的测试对象。我们的发现为科学家提供了路线图，帮助他们为他们的研究选择合适的工具。这种比较弥合了微生物学和计算分析之间的差距，精简研究工作。
