Single cell tracking based on Voronoi partition via stable matching Article Swipe
Young Hwan Chang
,
Jeremy W. Linsley
,
Josh Lamstein
,
Jaslin Kalra
,
Irina Epstein
,
Mariya Barch
,
Kenneth Daily
,
Phil Synder
,
Larsson Omberg
,
Laura M. Heiser
,
Steven Finkbeiner
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.08.20.259408
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.08.20.259408
Live-cell imaging is an important technique to study cell migration and proliferation as well as image-based profiling of drug perturbations over time. To gain biological insights from live-cell imaging data, it is necessary to identify individual cells, follow them over time and extract quantitative information. However, since often biological experiment does not allow the high temporal resolution to reduce excessive levels of illumination or minimize unnecessary oversampling to monitor long-term dynamics, it is still a challenging task to obtain good tracking results with coarsely sampled imaging data. To address this problem, we consider cell tracking problem as “stable matching problem” and propose a robust tracking method based on Voronoi partition which adapts parameters that need to be set according to the spatio-temporal characteristics of live cell imaging data such as cell population and migration. We demonstrate the performance improvement provided by the proposed method using numerical simulations and compare its performance with proximity-based tracking and nearest neighbor-based tracking.
Related Topics
Concepts
Voronoi diagram
Computer science
Tracking (education)
Artificial intelligence
Live cell imaging
Matching (statistics)
Oversampling
Population
Computer vision
Partition (number theory)
Pattern recognition (psychology)
Mathematics
Cell
Statistics
Psychology
Pedagogy
Geometry
Bandwidth (computing)
Demography
Computer network
Biology
Combinatorics
Genetics
Sociology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2020.08.20.259408
- https://www.biorxiv.org/content/biorxiv/early/2020/08/20/2020.08.20.259408.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3051670137
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3051670137Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2020.08.20.259408Digital Object Identifier
- Title
-
Single cell tracking based on Voronoi partition via stable matchingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-20Full publication date if available
- Authors
-
Young Hwan Chang, Jeremy W. Linsley, Josh Lamstein, Jaslin Kalra, Irina Epstein, Mariya Barch, Kenneth Daily, Phil Synder, Larsson Omberg, Laura M. Heiser, Steven FinkbeinerList of authors in order
- Landing page
-
https://doi.org/10.1101/2020.08.20.259408Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2020/08/20/2020.08.20.259408.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2020/08/20/2020.08.20.259408.full.pdfDirect OA link when available
- Concepts
-
Voronoi diagram, Computer science, Tracking (education), Artificial intelligence, Live cell imaging, Matching (statistics), Oversampling, Population, Computer vision, Partition (number theory), Pattern recognition (psychology), Mathematics, Cell, Statistics, Psychology, Pedagogy, Geometry, Bandwidth (computing), Demography, Computer network, Biology, Combinatorics, Genetics, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- References (count)
-
33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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