Automatic Image Analysis Pipeline for Studying Growth in Arabidopsis

  • Katya Mkrtchyan
  • Anirban Chakraborty
  • Min Liu
  • Amit Roy-Chowdhury
Part of the Computational Biology book series (COBO, volume 22)


The need for high-throughput quantification of cell growth and cell division in a multilayer, multicellular tissue necessitates the development of an automated image analysis pipeline that is capable of processing high volumes of live imaging microscopy data. In this work, we present such an image processing and analysis pipeline that combines cell image registration, segmentation, tracking, and cell resolution 3D reconstruction for confocal microscopy-based time-lapse volumetric image stacks. The first component of the pipeline is an automated landmark-based registration method that uses a local graph-based approach to select a number of landmark points from the images and establishes correspondence between them. Once the registration is acquired, the cell segmentation and tracking problem is jointly solved using an adaptive segmentation and tracking module of the pipeline, where the tracking output acts as an indicator of the quality of segmentation and in turn the segmentation can be improved to obtain better tracking results. In the last module of our pipeline, an adaptive geometric tessellation-based 3D reconstruction algorithm is described, where complete 3D structures of individual cells in the tissue are estimated from sparse sets of 2D cell slices, as obtained from the previous components of the pipeline. Through experiments on Arabidopsis shoot apical meristems, we show that each component in the proposed pipeline provides highly accurate results and is robust to ‘Z-sparsity’ in imaging and low SNR at parts of the collected image stacks.


Voronoi Diagram Shoot Apical Meristem Iterative Close Point Iterative Close Point Landmark Point 
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.



We gratefully acknowledge Prof. Venugopala Reddy from Plant Biology at the University of California, Riverside for providing us the datasets on which results are shown. This work was supported in part by the National Science Foundation Integrative Graduate Education and Research Traineeship (IGERT) in Video Bioinformatics (DGE-0903667). Katya Mkrtchyan is an IGERT Fellow.


  1. 1.
    Chakraborty A, Perales M, Reddy GV, Roy-Chowdhury AK (2013) Adaptive geometric tessellation for 3D reconstruction of anisotropically developing cells in multilayer tissues from sparse volumetric microscopy images. PLoS ONEGoogle Scholar
  2. 2.
    Liu M, Yadav RK, Roy-Chowdhury A, Reddy GV (2010) Automated tracking of stem cell lineages of Arabidopsis shoot apex using local graph matching. Plant JGoogle Scholar
  3. 3.
    Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach IntellGoogle Scholar
  4. 4.
    Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med ImagingGoogle Scholar
  5. 5.
    Viola P, Wells WM (1997) Alignment by maximization of mutual information. Int J Comput VisGoogle Scholar
  6. 6.
    Fernandez R, Das R, Mirabet V, Moscardi E, Traas J, Verdeil J, Malandain G, Godin C (2010) Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution. Nat MethodsGoogle Scholar
  7. 7.
    Besl P, McKay N (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach IntellGoogle Scholar
  8. 8.
    Sharp GC, Lee SW, Wehe DK (2002) Invariant features and the registration of rigid bodies. IEEE Trans Pattern Anal Mach IntellGoogle Scholar
  9. 9.
    Mkrtchyan K, Chakraborty A, Roy-Chowdhury A (2013) Automated registration of live imaging stacks of Arabidopsis. In: International symposium on biomedical imagingGoogle Scholar
  10. 10.
    Reddy GV, Meyerowitz EM (2005) Stem-cell homeostasis and growth dynamics can be uncoupled in the Arabidopsis shoot apex. ScienceGoogle Scholar
  11. 11.
    Chan T, Vese L (2001) Active contours without edges. IEEE Trans Image ProcessGoogle Scholar
  12. 12.
    Li K, Kanade T (2007) Cell population tracking and lineage construction using multiple-model dynamics filters and spatiotemporal optimization. Microscop Image Anal Appl BiolGoogle Scholar
  13. 13.
    Cunha AL, Roeder AHK, Meyerowitz EM (2010) Segmenting the sepal and shoot apical meristem of Arabidopsis thaliana. Annu Int Conf IEEE Eng Med Biol SocGoogle Scholar
  14. 14.
    Chui H (2000) A new algorithm for non-rigid point matching. IEEE Comput Soc Conf Comput Vis Pattern RecognGoogle Scholar
  15. 15.
    Gor V, Elowitz M, Bacarian T, Mjolsness E (2005) Tracking cell signals in fluorescent images. In: IEEE workshop on computer vision methods for bioinformaticsGoogle Scholar
  16. 16.
    Rangarajan A, Chui H, Bookstein FL (2005) The soft assign procrustes matching algorithm. Inf Process Med ImagGoogle Scholar
  17. 17.
    Liu M, Chakraborty A, Singh D, Gopi M, Yadav R, Reddy GV, Roy-Chowdhury A (2011) Adaptive cell segmentation and tracking for volumetric confocal microscopy images of a developing plant meristem. Mol PlantGoogle Scholar
  18. 18.
    Chakraborty A, Roy-Chowdhury A (2014) Context aware spatio-temporal cell tracking in densely packed multilayer tissues. Med Image AnalGoogle Scholar
  19. 19.
    Chakraborty A, Roy-Chowdhury A (2014) A conditional random field model for tracking in densely packed cell structures. IEEE Int Conf Image ProcessGoogle Scholar
  20. 20.
    Beucher S, Lantuejoul C (1979) Use of watersheds in contour detection. In: International workshop on image processing: realtime edge and motion detection/estimationGoogle Scholar
  21. 21.
    Najman L, Schmitt M (1994) Watershed of a continuous function. Signal ProcessGoogle Scholar
  22. 22.
    Marcuzzo M, Quelhas P, Campilho A, Mendonca AM, Campilho AC (2008) Automatic cell segmentation from confocal microscopy images of the Arabidopsis root. In: IEEE international symposium on biomedical imagingGoogle Scholar
  23. 23.
    Soille P (2003) Morphological image analysis: principles and applications, 2nd edn. Springer, New YorkGoogle Scholar
  24. 24.
    Nakahari T, Murakami M, Yoshida H, Miyamoto M, Sohma Y, Imai Y (1990) Decrease in rat submandibular acinar cell volume during ACh stimulation. Am J PhysiolGoogle Scholar
  25. 25.
    Farinas J, Kneen M, Moore M, Verkman AS (1997) Plasma membrane water permeability of cultured cells and epithelia measured by light microscopy with spatial filtering. J General PhysiolGoogle Scholar
  26. 26.
    Kawahara K, Onodera M, Fukuda Y (1994) A simple method for continuous measurement of cell height during a volume change in a single A6 cell. Jpn J PhysiolGoogle Scholar
  27. 27.
    Kwiatkowska D, Routier-Kierzkowska A (2009) Morphogenesis at the inflorescence shoot apex of Anagallis arvensis: surface geometry and growth in comparison with the vegetative shoot. J Exp BotanyGoogle Scholar
  28. 28.
    Tataw O, Liu M, Yadav R, Reddy V, Roy-Chowdhury A (2010) Pattern analysis of stem cell growth dynamics in the shoot apex of Arabidopsis. IEEE Int Conf Image ProcessGoogle Scholar
  29. 29.
    Zhu Q, Tekola P, Baak JP, Belikin JA (1994) Measurement by confocal laser scanning microscopy of the volume of epidermal nuclei in thick skin sections. Anal Quant Cytol HistolGoogle Scholar
  30. 30.
    Errington RJ, Fricker MD, Wood JL, Hall AC, White NS (1997) Four-dimensional imaging of living chondrocytes in cartilage using confocal microscopy: a pragmatic approach. Am J PhysiolGoogle Scholar
  31. 31.
    Chakraborty A, Yadav RK, Reddy GV, Roy-Chowdhury A (2011) Cell resolution 3D reconstruction of developing multilayer tissues from sparsely sampled volumetric microscopy images. IEEE Int Conf Bioinform BiomedGoogle Scholar
  32. 32.
    Mjolsness E (2006) The growth and development of some recent plant models: a viewpoint. J Plant Growth Regul (Springer)Google Scholar
  33. 33.
    Gor V, Shapiro BE, Jönsson H, Heisler M, Reddy GV, Meyerowitz EM, Mjolsness E (2005) A software architecture for developmental modelling in plants: the computable plant project. Bioinf Genome Regul StructGoogle Scholar
  34. 34.
    Boissonnat J, Wormser C, Yvinec M (2006) Curved Voronoi diagrams, effective computational geometry for curves and surfaces. Mathematics and visualization. SpringerGoogle Scholar
  35. 35.
    Khachiyan LG (1996) Rounding of polytopes in the real number model of computation. Math Methods Oper ResGoogle Scholar
  36. 36.
    Kumar P, Yildirim EA (2005) Minimum-volume enclosing ellipsoids and core sets. J Opt Theory ApplGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Katya Mkrtchyan
    • 1
  • Anirban Chakraborty
    • 2
  • Min Liu
    • 2
  • Amit Roy-Chowdhury
    • 2
  1. 1.Department of Computer ScienceUniversity of CaliforniaRiversideUSA
  2. 2.Department of Electrical EngineeringUniversity of CaliforniaRiversideUSA

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