3D motion analysis of keratin filaments in living cells

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 7623, Issue PART 1, 2010, Pages

  Herberich, Gerlind ^a   Windoffer, Reinhard ^b   Leube, Rudolf ^b   Aach, Til ^a  

^a RWTH AACHEN UNIVERSITY   (Germany)

^b UNIVERSITY HOSPITAL RWTH AACHEN   (Germany)

Author keywords

confocal laser scanning microscopy; cytoskeleton; fluorescence microscopy; Motion analysis; registration

Indexed keywords

3-D IMAGE; 3D MOTION ANALYSIS; 3D MOTION ESTIMATION; CELL MOVEMENT; CONFOCAL LASER SCANNING MICROSCOPE; CONFOCAL LASER SCANNING MICROSCOPY; CYTOSKELETON; DEFORMABLE REGISTRATION; DYNAMIC NETWORK; ELASTIC CABLE; EPITHELIAL CELLS; FILAMENT FORMATION; FILAMENT NETWORKS; FREE-FORM DEFORMATION; IMAGE DATA; IMAGE SERIES; IN-VITRO; INTERMEDIATE FILAMENTS; LINEAR PROGRAMMING TECHNIQUES; LIVING CELL; LOCAL DEFORMATIONS; MOTION ANALYSIS; NETWORK ORGANISATION; NONRIGID REGISTRATION; PATHOLOGICAL CONDITIONS; PRE-REGISTRATION; QUANTITATIVE MEASUREMENT; REGISTRATION; REGISTRATION PROCESS; TRANSFORMATION MODEL; VECTOR FIELDS;

ALGORITHMS; CONFOCAL MICROSCOPY; DEFORMATION; FLUORESCENCE; FLUORESCENCE MICROSCOPY; IMAGING SYSTEMS; LINEAR PROGRAMMING; MEDICAL IMAGING; MOTION ESTIMATION; OPTIMIZATION; PRESSURE EFFECTS; SCANNING; TIME SERIES;

THREE DIMENSIONAL;

EID: 79751506570     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.844148     Document Type: Conference Paper

References (22)

1. Magin, T., Vijayaraj, P., and Leube, R., "Structural and regulatory functions of keratins.," Exp Cell Res 313(10), 2021-32 (2007).
2. Windoffer, R., Woell, S., Strnad, P., and Leube, R. E., "Identification of novel principles of keratin filament network turnover in living cells.," Mol Biol Cell 15, 2436-2448 (May 2004).
3. Windoffer, R. and Leube, R. E., "De novo formation of cytokeratin filament networks originates from the cell cortex in a-431 cells.," Cell Motil Cytoskeleton 50, 33-44 (Sep 2001).
4. Klsch, A., Windoffer, R., and Leube, R. E., "Actin-dependent dynamics of keratin filament precursors.," Cell Motil Cytoskeleton 66, 976-985 (Nov 2009).
5. Danuser, G. and Waterman-Storer, C. M., "Quantitative fluorescent speckle microscopy of cytoskeleton dynamics.," Annu Rev Biophys Biomol Struct 35, 361-387 (2006).
6. Helmke, B. P., Thakker, D. B., Goldman, R. D., and Davies, P. F., "Spatiotemporal analysis of flow-induced intermediate filament displacement in living endothelial cells.," Biophys J 80, 184-194 (Jan 2001).
7. Raisch, F., Scharr, H., Kirchgener, N., Jaehne, B., Fink, R., and Uttenweiler, D., "Velocity and feature estimation of actin filaments using active contours in noisy fluorescence image sequences," in [Visualization, Imaging, and Image Processing - 2002], (2002).
8. Glocker, B., Komodakis, N., Tziritas, G., Navab, N., and Paragios, N., "Dense image registration through MRFs and efficient linear programming," Medical Image Analysis 12(6), 731-741 (2008).
9. Glocker, B., Paragios, N., Komodakis, N., Tziritas, G., and Navab, N., "Optical flow estimation with uncertainties through dynamic mrfs," in [Proc. IEEE Conference on Computer Vision and Pattern Recognition CVPR 2008], 1-8 (2008).
10. Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., and Hawkes, D. J., "Nonrigid registration using free-form deformations: application to breast MR images," IEEE Transactions on Medical Imaging 18(8), 712-721 (1999).
11. Komodakis, N., Tziritas, G., and Paragios, N., "Fast, approximately optimal solutions for single and dynamic MRFs," in [Proc. IEEE Conference on Computer Vision and Pattern Recognition CVPR '07], 1-8 (2007).
12. Komodakis, N. and Tziritas, G., "Approximate labeling via graph cuts based on linear programming," IEEE Transactions on Pattern Analysis and Machine Intelligence 29(8), 1436-1453 (2007).
13. Pawley, J. B., ed., [Handbook of Biological Confocal Microscopy], Springer Verlag (2006).
14. Windoffer, R. and Leube, R. E., "Imaging of keratin dynamics during the cell cycle and in response to phosphatase inhibition.," Methods Cell Biol 78, 321-352 (2004).
15. Dorn, J. F., Danuser, G., and Yang, G., "Computational processing and analysis of dynamic fluorescence image data.," Methods Cell Biol 85, 497-538 (2008).
16. Rohlfing, T., Maurer, C. R., J., Bluemke, D. A., and Jacobs, M. A., "Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint," IEEE Transactions on Medical Imaging 22, 730-741 (June 2003).
17. Bertero, M., Poggio, T. A., and Torre, V., "Ill-posed problems in early vision," Proceedings of the IEEE 76, 869-889 (Aug. 1988).
18. Terzopoulos, D., "Regularization of inverse visual problems involving discontinuities," IEEE Transactions on Pattern Analysis and Machine Intelligence , 413-424 (July 1986).
19. Modersitzki, J., [Numerical Methods for Image Registration], Oxford University Press (2004).
20. Li, S., [Markov Random Field Modeling in Image Analysis], Springer (2009).
21. Geman, S. and Geman, D., "Stochastic relaxation, gibbs distributions, and the bayesian restoration of images," IEEE Transactions on Pattern Analysis and Machine Intelligence , 721-741 (Nov. 1984).
22. Veksler, O., Efficient Graph-Based Energy Minimization Methods In Computer Vision, PhD thesis, Cornell University (1999).