Abstract
Automatic single-cell image analysis allows gaining deeper insights into biological processes. We present an approach for single-cell segmentation and tracking of bacterial cells in time-lapse microscopy image data. For cell segmentation we use linear feature detection and a probability map combined with schemes for cell splitting. For cell tracking we propose an approach based on the maximal overlapping area between cells, which is robust regarding cell rotation and accurately detects cell divisions. Our approach was successfully applied to segment and track cells in time-lapse images of the life cycle of Bacillus subtilis. We also quantitatively evaluated the performance of the segmentation and tracking approaches.
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References
Obara B, Roberts M, Armitage J, et al. Bacterial cell identification in differential interference contrast microscopy images. BMC Bioinformatics. 2013;14(134).
Battenberg E, Bischofs-Pfeifer I. A System for Automatic Cell Segmentation of Bacterial Microscopy Images. Technical Report, UC Berkeley; 2006.
Schmitter D, Wachowicz P, Sage D, et al. A 2D/3D image analysis system to track fluorescently labeled structures in rod-shaped cells: application to measure spindle pole asymmetry during mitosis. Cell Div. 2013;8(6):1–13.
Liu X, Harvey CW, Wang H, et al. Detecting and tracking motion of myxococcus xanthus bacteria in swarms. Lect Notes Computer Sci. 2012;7510:373–80.
Vallotton P, Sun C, Wang D, et al. Segmentation and tracking individual pseudomonas aeruginosa bacteria in dense populations of motile cells. Proc ICIVC. 2009; p. 221–5.
Vallotton P, Turnbull L, Whitchurch C, et al. Segmentation of dense 2D bacilli populations. Proc Int Conf on Digit Image Comp: Tech and Appl, Sydney, NSW. 2010; p. 82–6.
Zhang HP, Be’er A, Florin EL, et al. Collective motion and density fluctuations in bacterial colonies. Proc Natl Acad Sci, USA. 2010;107(31):13626–30.
Juand RR, Levchenko A, Burlina P. Tracking cell motion using GM-PHD. Proc ISBI. 2009; p. 1154–7.
Zuiderveld K. Contrast limited adaptive histograph equalization. In: Graphic Gems IV. Academic Press Professional, San Diego; 1994. p. 474–85.
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Liluashvili, V. et al. (2015). Automatic Single-Cell Segmentation and Tracking of Bacterial Cells in Fluorescence Microscopy Images. In: Handels, H., Deserno, T., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2015. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46224-9_42
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DOI: https://doi.org/10.1007/978-3-662-46224-9_42
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