Next Article in Journal
Identification of QR Code Perspective Distortion Based on Edge Directions and Edge Projections Analysis
Previous Article in Journal
Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks
Article

Investigating Optimal Time Step Intervals of Imaging for Data Quality through a Novel Fully-Automated Cell Tracking Approach

1
Department of Chemical and Process Engineering, University of Surrey, Stag Hill, University Campus, Guildford GU2 7XH, UK
2
Ibidi GmbH Lochhammer Schlag 11, 82166 Gräfelfing, Germany
3
Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK
4
School of Mathematical and Physical Sciences, Department of Mathematics, University of Sussex, Brighton BN1 9QH, UK
5
Department of Mathematics, University of Johannesburg, P.O. Box 524, Auckland Park 2006, Johannesburg, South Africa
*
Authors to whom correspondence should be addressed.
Received: 14 May 2020 / Revised: 20 June 2020 / Accepted: 2 July 2020 / Published: 7 July 2020
Computer-based fully-automated cell tracking is becoming increasingly important in cell biology, since it provides unrivalled capacity and efficiency for the analysis of large datasets. However, automatic cell tracking’s lack of superior pattern recognition and error-handling capability compared to its human manual tracking counterpart inspired decades-long research. Enormous efforts have been made in developing advanced cell tracking packages and software algorithms. Typical research in this field focuses on dealing with existing data and finding a best solution. Here, we investigate a novel approach where the quality of data acquisition could help improve the accuracy of cell tracking algorithms and vice-versa. Generally speaking, when tracking cell movement, the more frequent the images are taken, the more accurate cells are tracked and, yet, issues such as damage to cells due to light intensity, overheating in equipment, as well as the size of the data prevent a constant data streaming. Hence, a trade-off between the frequency at which data images are collected and the accuracy of the cell tracking algorithms needs to be studied. In this paper, we look at the effects of different choices of the time step interval (i.e., the frequency of data acquisition) within the microscope to our existing cell tracking algorithms. We generate several experimental data sets where the true outcomes are known (i.e., the direction of cell migration) by either using an effective chemoattractant or employing no-chemoattractant. We specify a relatively short time step interval (i.e., 30 s) between pictures that are taken at the data generational stage, so that, later on, we may choose some portion of the images to produce datasets with different time step intervals, such as 1 min, 2 min, and so on. We evaluate the accuracy of our cell tracking algorithms to illustrate the effects of these different time step intervals. We establish that there exist certain relationships between the tracking accuracy and the time step interval associated with experimental microscope data acquisition. We perform fully-automatic adaptive cell tracking on multiple datasets, to identify optimal time step intervals for data acquisition, while at the same time demonstrating the performance of the computer cell tracking algorithms. View Full-Text
Keywords: optimal time step intervals; microscope data acquisition; fully-automated cell tracking; phase-contrast microscopy; segmentation; particle tracking; chemotaxis; directed cell migration; tracking accuracy optimal time step intervals; microscope data acquisition; fully-automated cell tracking; phase-contrast microscopy; segmentation; particle tracking; chemotaxis; directed cell migration; tracking accuracy
Show Figures

Figure 1

MDPI and ACS Style

Yang, F.W.; Tomášová, L.; Guttenberg, Z.v.; Chen, K.; Madzvamuse, A. Investigating Optimal Time Step Intervals of Imaging for Data Quality through a Novel Fully-Automated Cell Tracking Approach. J. Imaging 2020, 6, 66. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6070066

AMA Style

Yang FW, Tomášová L, Guttenberg Zv, Chen K, Madzvamuse A. Investigating Optimal Time Step Intervals of Imaging for Data Quality through a Novel Fully-Automated Cell Tracking Approach. Journal of Imaging. 2020; 6(7):66. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6070066

Chicago/Turabian Style

Yang, Feng W., Lea Tomášová, Zeno v. Guttenberg, Ke Chen, and Anotida Madzvamuse. 2020. "Investigating Optimal Time Step Intervals of Imaging for Data Quality through a Novel Fully-Automated Cell Tracking Approach" Journal of Imaging 6, no. 7: 66. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6070066

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop