ViCAR: An Adaptive and Landmark-Free Registration of Time Lapse Image Data from Microfluidics Experiments

Hattab G, Schlüter J-P, Becker A, Nattkemper TW (2017)
Frontiers in Genetics 8: 69.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Hattab, GeorgesUniBi ; Schlüter, Jan-Philip; Becker, Anke; Nattkemper, Tim WilhelmUniBi
Abstract / Bemerkung
In order to understand gene function in bacterial life cycles, time lapse bioimaging is applied in combination with different marker protocols in so called microfluidics chambers (i.e., a multi-well plate). In one experiment, a series of T images is recorded for one visual field, with a pixel resolution of 60 nm/px. Any (semi-)automatic analysis of the data is hampered by a strong image noise, low contrast and, last but not least, considerable irregular shifts during the acquisition. Image registration corrects such shifts enabling next steps of the analysis (e.g., feature extraction or tracking). Image alignment faces two obstacles in this microscopic context: (a) highly dynamic structural changes in the sample (i.e., colony growth) and (b) an individual data set-specific sample environment which makes the application of landmarks-based alignments almost impossible. We present a computational image registration solution, we refer to as ViCAR: (Vi)sual (C)ues based (A)daptive (R)egistration, for such microfluidics experiments, consisting of (1) the detection of particular polygons (outlined and segmented ones, referred to as visual cues), (2) the adaptive retrieval of three coordinates throughout different sets of frames, and finally (3) an image registration based on the relation of these points correcting both rotation and translation. We tested ViCAR with different data sets and have found that it provides an effective spatial alignment thereby paving the way to extract temporal features pertinent to each resulting bacterial colony. By using ViCAR, we achieved an image registration with 99.9% of image closeness, based on the average rmsd of 4.10−2 pixels, and superior results compared to a state of the art algorithm.
Erscheinungsjahr
2017
Zeitschriftentitel
Frontiers in Genetics
Band
8
Art.-Nr.
69
ISSN
1664-8021
Finanzierungs-Informationen
Open-Access-Publikationskosten wurden durch die Deutsche Forschungsgemeinschaft und die Universität Bielefeld gefördert.
Page URI
https://pub.uni-bielefeld.de/record/2911830

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Hattab G, Schlüter J-P, Becker A, Nattkemper TW. ViCAR: An Adaptive and Landmark-Free Registration of Time Lapse Image Data from Microfluidics Experiments. Frontiers in Genetics. 2017;8: 69.
Hattab, G., Schlüter, J. - P., Becker, A., & Nattkemper, T. W. (2017). ViCAR: An Adaptive and Landmark-Free Registration of Time Lapse Image Data from Microfluidics Experiments. Frontiers in Genetics, 8, 69. doi:10.3389/fgene.2017.00069
Hattab, G., Schlüter, J. - P., Becker, A., and Nattkemper, T. W. (2017). ViCAR: An Adaptive and Landmark-Free Registration of Time Lapse Image Data from Microfluidics Experiments. Frontiers in Genetics 8:69.
Hattab, G., et al., 2017. ViCAR: An Adaptive and Landmark-Free Registration of Time Lapse Image Data from Microfluidics Experiments. Frontiers in Genetics, 8: 69.
G. Hattab, et al., “ViCAR: An Adaptive and Landmark-Free Registration of Time Lapse Image Data from Microfluidics Experiments”, Frontiers in Genetics, vol. 8, 2017, : 69.
Hattab, G., Schlüter, J.-P., Becker, A., Nattkemper, T.W.: ViCAR: An Adaptive and Landmark-Free Registration of Time Lapse Image Data from Microfluidics Experiments. Frontiers in Genetics. 8, : 69 (2017).
Hattab, Georges, Schlüter, Jan-Philip, Becker, Anke, and Nattkemper, Tim Wilhelm. “ViCAR: An Adaptive and Landmark-Free Registration of Time Lapse Image Data from Microfluidics Experiments”. Frontiers in Genetics 8 (2017): 69.
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2019-09-25T06:48:08Z
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2 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

SeeVis-3D space-time cube rendering for visualization of microfluidics image data.
Hattab G, Nattkemper TW., Bioinformatics 35(10), 2019
PMID: 30346487
A Novel Methodology for Characterizing Cell Subpopulations in Automated Time-lapse Microscopy.
Hattab G, Wiesmann V, Becker A, Munzner T, Nattkemper TW., Front Bioeng Biotechnol 6(), 2018
PMID: 29541635

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