3D Dendrite Reconstruction and Spine Identification

  • Wengang Zhou
  • Houqiang Li
  • Xiaobo Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)


In neuron-biology, 3D neuron dendrite reconstruction followed by spine identification is indispensable for the study of neuronal functions and biophysical properties. In this paper, we propose an automatic dendrite reconstruction method to with a surface representation of the neuron on the basis of a novel level set approach. Our novel level set approach can effectively tackle the problem of segmentation under blurring and intensity in-homogeneity. Then spines are detected based on dendrite medial axis and a label-based thinning strategy is proposed to accurately extract the dendrite skeleton for spine identification. Experimental results reveal that our method works well.


Dendrite spine reconstruction level set segmentation skeleton 


  1. 1.
    Nimchinsky, A., Sabatini, B.L., Svoboda, K.: Structure and function of dendritic spines. Annu. Rev. Phusiol. 64, 313–353 (2002)CrossRefGoogle Scholar
  2. 2.
    Carlbom, I., et al.: Computer-assisted registration, segmentation, and 3D reconstruction from images of neuronal tissue sections. IEEE Trans. on Medical Imaging (1994)Google Scholar
  3. 3.
    Uehara, C., et al.: Towards automatic reconstruction of dendrite morphology from live neurons. In: IEEE Conference of the Engineering in Medicine and Biology Society (2004)Google Scholar
  4. 4.
    He, W., et al.: Automated three-dimensional tracing of hrp stained neurons from a stack of brightfield optical slices. Microscopy and Microanalysis 9, 296–310 (2003)CrossRefGoogle Scholar
  5. 5.
    Weaver, C.M., et al.: Automated Algorithms for Multiscale Morphometry of Neuronal Dendrites. Neural Computation 16, 1353–1383 (2004)CrossRefzbMATHGoogle Scholar
  6. 6.
    Al-Kofahi, K.A., et al.: Rapid automated three-dimensional tracing of neurons from confocal image stacks. IEEE Trans. Infor. Tech. Bio. 6(2), 171–187 (2002)CrossRefGoogle Scholar
  7. 7.
    Koh, Y.Y., Lindquist, W.B., Zito, K.: An image analysis algorithm for dendritic spines. Neural Computation 14, 1283–1310 (2002)CrossRefzbMATHGoogle Scholar
  8. 8.
    Li, C., et al.: Implicit Active Contour Driven by Local Binary Fitting Energy. In: IEEE Conf. Comput. Vis Pattern Recognit., pp. 1–7 (2007)Google Scholar
  9. 9.
    Palagyi, K., Kuba, A.: A 3D 6-subiteration thinning algorithm for extracting medial lines. Pattern Recognition letters 19 (1998)Google Scholar
  10. 10.
    Zhou, W., et al.: A new algorithm for 3D dendritic spine detection. In: International Symposium on Computational Models of Life Sciences, pp. 137–146 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Wengang Zhou
    • 1
  • Houqiang Li
    • 1
  • Xiaobo Zhou
    • 2
  1. 1.Department of EEISUniversity of Science and Technology of ChinaHefeiP.R. China
  2. 2.Center of Biotechnology and Informatics, The Methodist HospitalResearch Institute & Weill Medical College of Cornell UniversityHouston

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