TRAgen: A Tool for Generation of Synthetic Time-Lapse Image Sequences of Living Cells

  • Vladimír Ulman
  • Zoltán Orémuš
  • David Svoboda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9279)


In biomedical image processing, correct tracking of individual cells is important task for the study of dynamic cellular processes. It is, however, often difficult to decide whether obtained tracking results are correct or not. This is mainly due to complexity of the data that can show hundreds of cells, due to improper data sampling either in time or in space, or when the time-lapse sequence consists of blurred noisy images. This prohibits manual extraction of reliable ground truth (GT) data as well. Nonetheless, if reliable testing data with GT were available, one could compare the results of the examined tracking algorithm with the GT and assess its performance quantitatively.

In this paper, we introduce a novel versatile tool capable of generating 2D image sequences showing simulated living cell populations with GT for evaluation of biomedical tracking. The simulated events include namely cell motion, cell division, and cell clustering up to tissue-level density. The method is primarily designed to operate at inter-cellular scope.


Biomedical imaging Simulation Evaluation Cell tracking 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ananthakrishnan, R., Ehrlicher, A.: The forces behind cell movement. International Journal of Biological Sciences 3(5), 303–317 (2007)CrossRefGoogle Scholar
  2. 2.
    Buck, T., Li, J., Rohde, G., Murphy, R.: Toward the virtual cell: Automated approaches to building models of subcellular organization “learned” from microscopy images. Bioessays 34(9), 791–799 (2012)CrossRefGoogle Scholar
  3. 3.
    Coelho, L.P., Shariff, A., Murphy, R.F.: Nuclear segmentation in microscope cell images: A hand-segmented dataset and comparison of algorithms. In: ISBI, pp. 518–521 (2009)Google Scholar
  4. 4.
    Dufour, A., Shinin, V., Tajbakhsh, S., Guillen-Aghion, N., Olivo-Marin, J.C., Zimmer, C.: Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces. IEEE Trans. on Image Processing 14(9), 1396–1410 (2005)CrossRefGoogle Scholar
  5. 5.
    Dufour, A., Thibeaux, R., Labruyere, E., Guillén, N., Olivo-Marin, J.: 3-D active meshes: fast discrete deformable models for cell tracking in 3-D time-lapse microscopy. IEEE Trans. on Medical Imaging 20(7), 1925–1937 (2010)Google Scholar
  6. 6.
    Duives, D.C., Daamen, W., Hoogendoorn, S.P.: State-of-the-art crowd motion simulation models. Transportation Research Part C: Emerging Technologies 37, 193–209 (2013)CrossRefGoogle Scholar
  7. 7.
    Dzyubachyk, O., van Cappellen, W.A., Essers, J., Niessen, W.J., Meijering, E.: Advanced level-set-based cell tracking in time-lapse fluorescence microscopy. IEEE Trans. on Medical Imaging 29(3), 852–867 (2010)CrossRefGoogle Scholar
  8. 8.
    Gelasca, E.D., Byun, J., Obara, B., Manjunath, B.: Evaluation and benchmark for biological image segmentation. In: ICIP, October 2008Google Scholar
  9. 9.
    Helbing, D., Farkas, I., Molnar, P., Vicsek, T.: Simulation of pedestrian crowds in normal and evacuation situations. Pedestrian and Evacuation Dynamics 21, 21–58 (2002)Google Scholar
  10. 10.
    Lehmussola, A., Ruusuvuori, P., Selinummi, J., Huttunen, H., Yli-Harja, O.: Computational framework for simulating fluorescence microscope images with cell populations. IEEE Trans. Med. Imaging 26(7), 1010–1016 (2007)CrossRefGoogle Scholar
  11. 11.
    Li, K., Miller, E., Chen, M., Kanade, T., Weiss, L., Campbell, P.: Cell population tracking and lineage construction with spatiotemporal context. Medical Image Analysis 12(5), 546–566 (2008)CrossRefGoogle Scholar
  12. 12.
    Lihavainen, E., Mäkelä, J., Spelbrink, J., Ribeiro, A.: Mytoe: automatic analysis of mitochondrial dynamics. Bioinformatics 28(7), 1050–1051 (2012)CrossRefGoogle Scholar
  13. 13.
    Ljosa, V., Sokolnicki, K.L., Carpenter, A.E.: Annotated high-throughput microscopy image sets for validation. Nat. Methods 9(7), 637 (2012)CrossRefGoogle Scholar
  14. 14.
    Magnusson, K., Jalden, J.: A batch algorithm using iterative application of the viterbi algorithm to track cells and construct cell lineages. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 382–385, May 2012Google Scholar
  15. 15.
    Malm, P., Brun, A., Bengtsson, E.: Simulation of bright-field microscopy images depicting pap-smear specimen. Cytometry Part A 87(3), 212–226 (2015)CrossRefGoogle Scholar
  16. 16.
    Maška, M., Ulman, V., Svoboda, D., Matula, P., Matula, P., et al.: A benchmark for comparison of cell tracking algorithms. Bioinformatics, pp. 1609–1617 (2014)Google Scholar
  17. 17.
    Perlin, K.: An image synthesizer. In: SIGGRAPH 1985: Proceedings of the 12th Annual Conference on Computer Graphics and Interactive Techniques, pp. 287–296. ACM Press, New York (1985)Google Scholar
  18. 18.
    Rajaram, S., Pavie, B., Hac, N.E.F., Altschuler, S.J., Wu, L.F.: Simucell: a flexible framework for creating synthetic microscopy images. Nat. Methods 9(7), 634–635 (2012)CrossRefGoogle Scholar
  19. 19.
    Reece, J., Urry, L., Cain, M., Wasserman, S., Minorsky, P., Jackson, R.: Campbell Biology, 9th edn. Pearson Benjamin Cummings (2011)Google Scholar
  20. 20.
    Romanczuk, P., Bär, M., Ebeling, W., Lindner, B., Schimansky-Geier, L.: Active Brownian particles. The European Physical Journal - Special Topics 202(1), 1–162 (2012)CrossRefGoogle Scholar
  21. 21.
    Ruusuvuori, P., Lehmussola, A., Selinummi, J., Rajala, T., Huttunen, H., Yli-Harja, O.: Benchmark set of synthetic images for validating cell image analysis algorithms. In: Proceedings of the 16th European Signal Processing Conference, EUSIPCO (2008)Google Scholar
  22. 22.
    Svoboda, D., Ulman, V.: Towards a realistic distribution of cells in synthetically generated 3d cell populations. In: Petrosino, A. (ed.) ICIAP 2013, Part II. LNCS, vol. 8157, pp. 429–438. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  23. 23.
    Svoboda, D., Kozubek, M., Stejskal, S.: Generation of digital phantoms of cell nuclei and simulation of image formation in 3d image cytometry. Cytometry Part A 75A(6), 494–509 (2009)CrossRefGoogle Scholar
  24. 24.
    Svoboda, D., Ulman, V.: Generation of synthetic image datasets for time-lapse fluorescence microscopy. In: Campilho, A., Kamel, M. (eds.) ICIAR 2012, Part II. LNCS, vol. 7325, pp. 473–482. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  25. 25.
    Szabo, A., Perryn, E., Czirok, A.: Network formation of tissue cells via preferential attraction to elongated structures. Phys. Rev. Lett. 98, 038102 (2007)CrossRefGoogle Scholar
  26. 26.
    Tektonidis, M., Kim, I.H., Chen, Y.C.M., Eils, R., Spector, D.L., Rohr, K.: Non-rigid multi-frame registration of cell nuclei in live cell fluorescence microscopy image data. Medical Image Analysis 19(1), 1–14 (2015)CrossRefGoogle Scholar
  27. 27.
    Webb, D., Hamilton, M.A., Harkin, G.J., Lawrence, S., Camper, A.K., Lewandowski, Z.: Assessing technician effects when extracting quantities from microscope images. Journal of Microbiological Methods 53(1), 97–106 (2003)CrossRefGoogle Scholar
  28. 28.
    Xiong, W., Wang, Y., Ong, S.H., Lim, J.H., Jiang, L.: Learning cell geometry models for cell image simulation: An unbiased approach. In: ICIP, pp. 1897–1900 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Vladimír Ulman
    • 1
  • Zoltán Orémuš
    • 1
  • David Svoboda
    • 1
  1. 1.Centre for Biomedical Image AnalysisMasaryk UniversityBrnoCzech Republic

Personalised recommendations