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An Expectation Maximization Based Method for Subcellular Particle Tracking Using Multi-angle TIRF Microscopy

  • Liang Liang
  • Hongying Shen
  • Pietro De Camilli
  • Derek K. Toomre
  • James S. Duncan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6891)

Abstract

Multi-angle total internal reflection fluorescence microscopy (MA-TIRFM) is a new generation of TIRF microscopy to study cellular processes near dorsal cell membrane in 4 dimensions (3D+t). To perform quantitative analysis using MA-TIRFM, it is necessary to track subcellular particles in these processes. In this paper, we propose a method based on a MAP framework for automatic particle tracking and apply it to track clathrin coated pits (CCPs). The expectation maximization (EM) algorithm is employed to solve the MAP problem. To provide the initial estimations for the EM algorithm, we develop a forward filter based on the most probable trajectory (MPT) filter. Multiple linear models are used to model particle dynamics. For CCP tracking, we use two linear models to describe constrained Brownian motion and fluorophore variation according to CCP properties. The tracking method is evaluated on synthetic data and results show that it has high accuracy. The result on real data confirmed by human expert cell biologists is also presented.

Keywords

Expectation Maximization Mean Absolute Percentage Error Expectation Maximization Algorithm Clathrin Mediate Endocytosis Total Internal Reflection Fluorescence Microscopy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Slepnev, V.I., De Camilli, P.: Accessory factors in clathrin-dependent synaptic vesicle endocytosis. Nature Reviews Neuroscience 1, 161–172 (2000)CrossRefGoogle Scholar
  2. 2.
    Brandenburg, B., Zhuang, X.: Virus trafficking - learning from single-virus tracking. Nature Reviews Microbiology 5, 197–208 (2007)CrossRefGoogle Scholar
  3. 3.
    Yang, Q., Karpikov, A., Toomre, D., Duncan, J.S.: 3D reconstruction of microtubules from multi-angle total internal reflection fluorescence microscopy using Bayesian framework. IEEE Trans. on Image Processing (2011) (in press)Google Scholar
  4. 4.
    Smal, I., Niessen, W., Meijering, E.: A new detection scheme for multiple object tracking in fluorescence microscopy by joint probabilistic data association filtering. In: IEEE Int. Symposium on Biomedical Imaging: From Nano to Macro, pp. 264–267 (2008)Google Scholar
  5. 5.
    Genovesio, A., Liedl, T., Emiliani, V., Parak, W.J., Coppey-Moisan, M., Olivo-Marin, J.-C.: Multiple particle tracking in 3-D+t microscopy: method and application to the tracking of endocytosed quantum dots. IEEE Trans. on Image Processing 15(5), 1062–1070 (2006)CrossRefGoogle Scholar
  6. 6.
    Liang, L., Shen, H., De Camilli, P., Duncan, J.S.: Tracking clathrin coated pits with a multiple hypothesis based method. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 315–322. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Jaqaman, K., Loerke, D., Mettlen, M., Kuwata, H., Grinstein, S., Schmid, S.L.L., Danuser, G.: Robust single-particle tracking in live-cell time-lapse sequences. Nature methods 5, 695–702 (2008)CrossRefGoogle Scholar
  8. 8.
    Li, X.R., Jilkov, V.P.: Survey of maneuvering target tracking. Part I. Dynamic models. IEEE Trans. on Aerospace and Electronic Systems 39(4), 1333–1364 (2003)CrossRefGoogle Scholar
  9. 9.
    Zhang, Q.: Optimal filtering of discrete-time hybrid systems. Journal of Optimization Theory and Applications 100(1), 123–144 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Rangarajan, A., Chui, H., Bookstein, F.L.: The softassign procrustes matching algorithm. In: Duncan, J.S., Gindi, G. (eds.) IPMI 1997. LNCS, vol. 1230, pp. 29–42. Springer, Heidelberg (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Liang Liang
    • 1
  • Hongying Shen
    • 1
  • Pietro De Camilli
    • 1
  • Derek K. Toomre
    • 1
  • James S. Duncan
    • 1
  1. 1.Yale UniversityNew HavenUSA

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