Abstract
Automated segmentation and tracking of cells in time-lapse imaging is a process of fundamental significance in several biomedical applications. In this work our interest is focused on cell segmentation over a set of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We utilize a region-based approach to curve evolution based on the level-set formulation. We introduce and test the use of temporal linking for level-set initialization to improve the robustness and computational time of level-set convergence. We validate our segmentation approach against manually segmented images provided by the Cell Tracking Challenge consortium. Our method produces encouraging segmentation results with an average DICE score of 0.78 over a variety of simulated and real sequences and speeds up the convergence rate by an average factor of 10.2.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Eils, R., Athale, C.: Computational imaging in cell biology. J. Cell. Biol. 161(3), 477–481 (2003)
Stephens, D.J., Allan, V.J.: Light microscopy techniques for live cell imaging. Science 300(5616), 82–86 (2003)
Meijering, E., Dzyubachyk, O., Smal, I.: Methods for cell and particle tracking. Methods Enzymo. 504, 183–200 (2012)
Dzyubachyk, O., van Cappellen, W.A., Essers, J., Niessen, W.J., Meijering, E.H.W.: Advanced Level-Set-Based Cell Tracking in Time-Lapse Fluorescence Microscopy. IEEE Trans. Med. Imaging 29(3), 852–867 (2010)
Yang, X., Li, H., Zhou, X.: Nuclei Segmentation Using Marker-Controlled Watershed, Tracking Using Mean-Shift, and Kalman Filter in Time-Lapse Microscopy. IEEE Transactions on Circuits and Systems I: Regular Papers 53(11), 2405–2414 (2006)
Maška, M., et al.: A benchmark for comparison of cell tracking algorithms. Bioinformatics 30(11), 1609–1617 (2014)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)
Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape Modeling with Front Propagation: A Level-set Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 158–175 (1995)
Cremers, D., Rousson, M., Deriche, R.: A Review of Statistical Approaches to Level-set Segmentation: Integrating Color, Texture, Motion and Shape. International Journal of Computer Vision 72(2), 195–215 (2006)
Chan, T.F., Vese, L.A.: Active Contours Without Edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)
Cell Tracking Challenge (2013), http://www.grand-challenge.org/
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)
Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Comm. on Pure and Applied Mathematics 42(5), 577–685 (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Boukari, F., Makrogiannis, S. (2014). Spatio-temporal Level-Set Based Cell Segmentation in Time-Lapse Image Sequences. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-14364-4_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14363-7
Online ISBN: 978-3-319-14364-4
eBook Packages: Computer ScienceComputer Science (R0)