Abstract
.Significance: Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow for a better and more specific classification.Aim: We propose a methodology for cell classification based on the time-lapse quantitative phase images (QPIs) gained by digital holographic microscopy (DHM) with the goal of increasing performance of classification of dynamic cellular processes.Approach: The methodology was demonstrated by studying epithelial–mesenchymal transition (EMT) which entails major and distinct time-dependent morphological changes. The time-lapse QPIs of EMT were obtained over a 48-h period and specific novel features representing the dynamic cell behavior were extracted. The two distinct end-state phenotypes were classified by several supervised machine learning algorithms and the results were compared with the classification performed on single-time-point images.Results: In comparison to the single-time-point approach, our data suggest the incorporation of temporal information into the classification of cell phenotypes during EMT improves performance by nearly 9% in terms of accuracy, and further indicate the potential of DHM to monitor cellular morphological changes.Conclusions: Proposed approach based on the time-lapse images gained by DHM could improve the monitoring of live cell behavior in an automated fashion and could be further developed into a tool for high-throughput automated analysis of unique cell behavior.
Highlights
Automated image acquisition systems enable microscopic experiments that generate large image datasets
This study demonstrated that the quantitative nature of single-time-point images acquired by coherence-controlled holographic microscope (CCHM) improves the classification of cellular morphologies as compared to other techniques.[13,14]
The classification was performed on the features extracted from the single-time-point quantitative phase images (QPIs) to evaluate the contribution of the methodology based on time-lapse QPI
Summary
Automated image acquisition systems enable microscopic experiments that generate large image datasets. Manual observation and evaluation of the microscopic images require a Journal of Biomedical Optics. Strbkova et al.: Automated interpretation of time-lapse quantitative phase image by machine learning. Human analysis can be biased, varying with skill and scientific rigor. These and other aspects impose significant constraints on the speed, reliability, and validity of such evaluation of microscopic images. One approach to address these limitations is supervised machine learning,[1] which is increasingly being applied to the classification of microscopic data.[2,3] As an objective unbiased method of scoring the content of microscopic images, this method has been argued to be more sensitive, consistent, and accurate in comparison to subjective manual interpretation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
