Abstract
Cell cycle study using time-lapse fluorescent microscopy images is important for understanding the mechanisms of cell division and screening of anti-cancer drugs. Cell tracking is necessary for quantifying cell behaviors. However, the complex behaviors and similarity of individual cells in a dense population make the cell population tracking challenging. To deal with these challenges, we propose a novel tracking algorithm, in which the local neighboring information is introduced to distinguish the nearby cells with similar morphology, and the Interacting Multiple Model (IMM) filter is employed to compensate for cell migrations. Based on a similarity metric, integrating the local neighboring information, migration prediction, shape and intensity, the integer programming is used to achieve the most stable association between cells in two consecutive frames. We evaluated the proposed method on the high content screening assays of HeLa cancer cell populations, and achieved 92% average tracking accuracy.
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
Haggarty, S., Mayer, T., Miyamoto, D., Fathi, R., King, R., Mitchison, T., Schreiber, S.: Dissecting cellular processes using small molecules: identification of colchicine-like, taxol-like and other small molecules that perturb mitosis. Chemistry and Biology 7, 275–286 (2000)
Wang, M., Zhou, X., Li, F., Huckins, J., King, R.W., Wong, S.T.: Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy. Bioinformatics 24, 94–101 (2008)
Bunyak, F., Palaniappan, K., Nath, S.K., Baskin, I.T., Dong, G.: Quantitative cell motility for in vitro wound healing using level set-based active contour tracking. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1040–1043 (2006)
Zimmer, C., Labruyère, E., Meas-Yedid, V., Guillén, N., Olivo-Marin, J.: Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing. IEEE transactions on medical imaging 21, 1212–1221 (2002)
Li, K., Miller, E.D., Chen, M., Kanade, T., Weiss, L.E., Campbell, P.G.: Cell population tracking and lineage construction with spatiotemporal context. Med. Image Anal. 12, 546–566 (2008)
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. Image Process. 15, 1062–1070 (2006)
Debeir, O., Ham, P.V., Kiss, R., Decaestecker, C.: Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes. IEEE transactions on medical imaging 24, 697–711 (2005)
Bai, W., Zhou, X., Ji, L., Cheng, J., Wong, S.T.: Automatic dendritic spine analysis in two-photon laser scanning microscopy images. Cytometry A 71, 818–826 (2007)
Yan, J., Zhou, X., Yang, Q., Liu, N., Cheng, Q., Wong, S.T.C.: An effective system for optical microscopy cell image segmentation, tracking and cell phase identification. In: IEEE International Conference on Image Processing, Atlanta Marriott Marquis, Atlanta, pp. 1917–1920 (2006)
Al-Kofahi, O., Radke, R.J., Goderie, S.K., Shen, Q., Temple, S., Roysam, B.: Automated cell lineage construction. Cell Cycle 5, 327–335 (2006)
Chen, X., Zhou, X., Wong, S.T.C.: Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. IEEE Transactions on Biomedical Engineering 53, 762–766 (2006)
Jones, T., Carpener, A., Golland, P.: voronoi-based segmentation of cells on image manifolds. LNCS, pp. 535–543. Springer, Heidelberg (2005)
Blom, H.A.P.: An efficient filter for abruptly changing systems. In: Processings of 23rd IEEE Conference on Decision and Control, pp. 656–658 (1984)
Jiyun, B., Vu, N., Sumengen, B., Manjunath, B.S.: Quantitative analysis of immunofluorescent retinal images. In: IEEE International Symposium on Biomedical Imaging: Nano to Macro, Crystal Gateway Marriott, Arlington, Virginia (2006)
Li, F., Zhou, X., Zhu, J., Ma, J., Huang, X., Wong, S.T.: High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles. BMC Biotechnol. 7, 66 (2007)
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of ICML San Francisco, CA (2001), pp. 282–289 (2001)
Bilgin, C.C., Bullough, P., Plopper, G.E., Yener, B.: ECM-aware cell-graph mining for bone tissue modeling and classification. Data Mining and Knowledge Discovery 20, 416–438 (2010)
MetaXpress, http://www.moleculardevices.com/pages/software/metaxpress.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, F., Zhou, X., Wong, S.T.C. (2010). Optimal Live Cell Tracking for Cell Cycle Study Using Time-Lapse Fluorescent Microscopy Images. In: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds) Machine Learning in Medical Imaging. MLMI 2010. Lecture Notes in Computer Science, vol 6357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15948-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-15948-0_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15947-3
Online ISBN: 978-3-642-15948-0
eBook Packages: Computer ScienceComputer Science (R0)