Abstract
The Large Scale Digital Cell Analysis System (LSDCAS) developed at the University of Iowa provides capabilities for extended-time live cell image acquisition. This paper presents a new approach to quantitative analysis of live cell image data. By using time as an extra dimension, level set methods are employed to determine cell trajectories from 2D + time data sets. When identifying the cell trajectories, cell cluster separation and mitotic cell detection steps are performed. Each of the trajectories corresponds to the motion pattern of an individual cell in the data set. At each time frame, number of cells, cell locations, cell borders, cell areas, and cell states are determined and recorded. The proposed method can help solving cell analysis problems of general importance including cell pedigree analysis and cell tracking. The developed method was tested on cancer cell image sequences and its performance compared with manually-defined ground truth. The similarity Kappa Index is 0.84 for segmentation area and the signed border positioning segmentation error is 1.6 ± 2.1 μm.
Chapter PDF
Similar content being viewed by others
Keywords
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
Ianzini, F., Mackey, M.: Development of the Large-Scale Digital Cell Analysis System. Radiation Protection and Dosimetry 99, 81–94 (2002)
Wu, H., Gil, J., Barba, J.: Optimal segmentation of cell images. In: Proceedings of IEEE Vision, Image and Signal Processing, vol. 145, pp. 50–56. IEEE, Los Alamitos (1998)
Anoraganingrum, D.: Cell segmentation with median filter and mathematical morphology operation. In: Proceedings of 1999 International Conference on Image Analysis and Processing, pp. 1043–1046 (1999)
Yang, F., Mackey, M., Ianzini, F., Gallardo, G., Sonka, M.: Segmentation and quantitative analysis of the living tumor cells using Large Scale Digital Cell Analysis System. In: Proceedings of SPIE Conference on Medical Imaging, vol. 5370, pp. 1755–1763 (2004)
Zimmer, C., Labruyere, E., Meas-Yedid, V., Guillen, N., Olivo-Marin, J.C.: 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)
Kirubarajan, T., Bar-Shalom, Y., Pattipati, K.R.: Multiassignment for tracking a large number of overlapping objects. Multitarget-Multisensor Tracking: Applications and Advances III, 199–231 (2000)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. International Journal of Computer Vision, 321–331 (1988)
Leymarie, F., Levine, M.: Tracking deformable objects in the plane using an active contour model. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 617–634 (1993)
Sethian, J.: Level Set Methods and Fast Marching Methods Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press, Cambridge (1999)
Spyridonos, P., Glotsos, D., Cavouras, D., Ravazoula, P., Zolota, V., Nikiforidis, G.: Pattern recognition based segmentation method of cell nuclei in tissue section analysis. In: Proceedings of 14th IEEE International Conference on Digital Signal Processing (DSP 2002), pp. 1121–1124 (2002)
Nattkemper, T.W., Ritter, H.J., Schubert, W.: A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections. IEEE Transactions on Information Technology in Biomedicine 5, 138–149 (2001)
Lassouaoui, N., Hamami, L.: Genetic algorithms and multifractal segmentation of cervical cell images. In: Proceedings of Seventh International Symposium on Signal Processing and Its Applications, pp. 1–4 (2003)
Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recognition 19, 41–47 (1986)
Stoev, S.L.: Rafsi - a fast watershed algorithm based on rainfalling simulation. In: Proceedings of 8-th International Conference on Computer Graphics, Visualization, and Interactive Digital Media (WSCG 2000), pp. 100–107 (2000)
Zildenbos, A., Dawant, B., Margolin, R.: Morphometric analysis of white matter lesions in MR images: Method and validation. IEEE Transactions on Medical Imaging 13, 716–724 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, F., Mackey, M.A., Ianzini, F., Gallardo, G., Sonka, M. (2005). Cell Segmentation, Tracking, and Mitosis Detection Using Temporal Context. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566465_38
Download citation
DOI: https://doi.org/10.1007/11566465_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29327-9
Online ISBN: 978-3-540-32094-4
eBook Packages: Computer ScienceComputer Science (R0)