Wiggins, L. orcid.org/0000-0003-4615-2379, O'Toole, P.J., Brackenbury, W.J. et al. (1 more author) (2025) Exploring the impact of variability in cell segmentation and tracking approaches. Microscopy Research and Technique, 88 (3). pp. 716-731. ISSN 1059-910X
Abstract
Segmentation and tracking are essential preliminary steps in the analysis of almost all live cell imaging applications. Although the number of open-source software systems that facilitate automated segmentation and tracking continue to evolve, many researchers continue to opt for manual alternatives for samples that are not easily auto-segmented, tracing cell boundaries by hand and reidentifying cells on consecutive frames by eye. Such methods are subject to inter-user variability, introducing idiosyncrasies into the results of downstream analysis that are a result of subjectivity and individual expertise. The methods are also susceptible to intra-user variability, meaning findings are challenging to reproduce. In this pilot study, we demonstrate and quantify the degree of intra- and inter-user variability in manual cell segmentation and tracking by comparing the phenotypic metrics extracted from cells segmented and tracked by different members of our research team. Furthermore, we compare the segmentation results for a ptychographic cell image obtained using different automated software and demonstrate the high dependence of performance on the imaging modality they were developed to handle. Our results show that choice of segmentation and tracking methods should be considered carefully in order to enhance the quality and reproducibility of results.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s). Microscopy Research and Technique published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0/ |
Keywords: | automation; cell phenotyping; image analysis; manual; segmentation; tracking |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Chemical, Materials and Biological Engineering |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Nov 2024 11:50 |
Last Modified: | 12 Mar 2025 17:02 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1002/jemt.24715 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:219787 |