3D Automated Nuclear Morphometric Analysis Using Active Meshes

  • Alexandre Dufour
  • JooHyun Lee
  • Nicole Vincent
  • Regis Grailhe
  • Auguste Genovesio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)


Recent advances in bioimaging have allowed to observe biological phenomena in three dimensions in a precise and automated fashion. However, the analysis of depth-stacks acquired in fluorescence microscopy constitutes a challenging task and motivates the development of robust methods. Automated computational schemes to process 3D multi-cell images from High Content Screening (HCS) experiments are part of the next generation methods for drug discovery. Working toward this goal, we propose a fully automated framework which allows fast segmentation and 3D morphometric analysis of cell nuclei. The method is based on deformable models called Active Meshes, featuring automated initialization, robustness to noise, real-time 3D visualization of the objects during their analysis and precise geometrical shape measurements thanks to a parametric representation of each object. The framework has been tested on a low throughput microscope (classically found in research facilities) and on a fully automated imaging platform (used in screening facilities). We also propose shape descriptors and evaluate their robustness and independence on fluorescent beads and on two cell lines.


Active Contour Maximum Intensity Projection Active Mesh Shape Descriptor Deformable Model 
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.


  1. 1.
    Vonesch, C., Aguet, F., Vonesch, J.L., Unser, M.: The colored revolution of bioimaging. IEEE Signal Processing Magazine 23(3), 20–31 (2006)CrossRefGoogle Scholar
  2. 2.
    Leman, E., Getzenberg, R.: Nuclear matrix protein as biomarkers in prostate cancer. Journal of Cell Biochemistry 86(2), 213–223 (2002)CrossRefGoogle Scholar
  3. 3.
    Lorensen, W., Cline, H.: Marching cubes: a high resolution 3D surface construction algorithm. In: SIGGRAPH 1987. 14th annual conference on Computer graphics and interactive techniques, pp. 163–169. ACM Press, New York (1987)CrossRefGoogle Scholar
  4. 4.
    Zimmer, C., Zhang, B., Dufour, A., Thebaud, A., Berlemont, S., Meas-Yedid, V., Olivo-Marin, J.C.: On the digital trail of mobile cells. Signal Processing Magazine 23(3), 54–62 (2006)CrossRefGoogle Scholar
  5. 5.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1, 321–331 (1988)CrossRefGoogle Scholar
  6. 6.
    Sethian, J.A.: Level set methods and fast marching methods, 2nd edn. Cambridge University Press, Cambridge (1999)zbMATHGoogle Scholar
  7. 7.
    Malpica, N., de Solorzano, C.O.: Automated Nuclear Segmentation in Fluorescence Microscopy. In: Science, Technology and Education of Microscopy: an Overview. Microscopy Book Series, Formatex, vol. 2, pp. 614–621 (2002)Google Scholar
  8. 8.
    Dufour, A., Shinin, V., Tajbaksh, S., Guillen, N., Olivo-Marin, J., Zimmer, C.: Segmenting and tracking fluorescent cells in dynamic 3d microscopy with coupled active surfaces. IEEE Transactions on Image Processing 14(9), 1396–1410 (2005)CrossRefGoogle Scholar
  9. 9.
    Delingette, H.: General object reconstruction based on simplex meshes. International Journal on Computer Vision 32, 111–146 (1999)CrossRefGoogle Scholar
  10. 10.
    Lachaud, J., Montanvert, A.: Deformable meshes with automated topology changes for coarse-to-fine three-dimensional surface extraction. Medical Image Analysis 3(2), 187–207 (1999)CrossRefGoogle Scholar
  11. 11.
    Zhang, C., Chen, T.: Efficient feature extraction for 2D/3D objects in mesh representation. In: International Conference on Image Processing, Thessaloniki, pp. 935–938 (2001)Google Scholar
  12. 12.
    Zhukov, L., Bao, Z., Guskov, I., Wood, J., Breen, D.: Dynamic deformable models for 3D MRI heart segmentation. In: SPIE Medical Imaging, vol. 4684, pp. 1398–1405 (2002)Google Scholar
  13. 13.
    Dufour, A., Vincent, N., Genovesio, A.: 3D Mumford-Shah based active mesh. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds.) CIARP 2006. LNCS, vol. 4225, pp. 208–217. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure App. Math. 42, 577–684 (1989)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Zhang, B., Zimmer, C., Olivo-Marin, J.C.: Tracking fluorescent cells with coupled geometric active contours. In: International Symposium on Biomedical Imaging, Arlington, pp. 476–479 (2004)Google Scholar
  16. 16.
    Zimmer, C., Olivo-Marin, J.C.: Coupled Parametric Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(11), 1838–1842 (2005)CrossRefGoogle Scholar
  17. 17.
    Gueziec, A., Hummel, R.: Exploiting triangulated surface extraction using tetrahedral decomposition. IEEE Transactions on Visualization and Computer Graphics 1(4), 328–342 (1995)CrossRefGoogle Scholar
  18. 18.
    Hoeffding, W.: A class of statistics with asymptotically normal distribution. The Annals of Mathematical Statistics 19(3), 293–325 (1948)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Alexandre Dufour
    • 1
    • 3
  • JooHyun Lee
    • 2
  • Nicole Vincent
    • 3
  • Regis Grailhe
    • 2
  • Auguste Genovesio
    • 1
  1. 1.Image Mining Group, Institut PasteurKorea
  2. 2.Dynamic Imaging Platform, Institut PasteurKorea
  3. 3.Intelligent Perception Systems (SIP-CRIP5) team, Paris Descartes University 

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