Skip to main content
SpringerLink
Log in

MDL Constrained 3-D Grayscale Skeletonization Algorithm for Automated Extraction of Dendrites and Spines from Fluorescence Confocal Images

  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

This paper presents a method for improved automatic delineation of dendrites and spines from three-dimensional (3-D) images of neurons acquired by confocal or multi-photon fluorescence microscopy. The core advance presented here is a direct grayscale skeletonization algorithm that is constrained by a structural complexity penalty using the minimum description length (MDL) principle, and additional neuroanatomy-specific constraints. The 3-D skeleton is extracted directly from the grayscale image data, avoiding errors introduced by image binarization. The MDL method achieves a practical tradeoff between the complexity of the skeleton and its coverage of the fluorescence signal. Additional advances include the use of 3-D spline smoothing of dendrites to improve spine detection, and graph-theoretic algorithms to explore and extract the dendritic structure from the grayscale skeleton using an intensity-weighted minimum spanning tree (IW-MST) algorithm. This algorithm was evaluated on 30 datasets organized in 8 groups from multiple laboratories. Spines were detected with false negative rates less than 10% on most datasets (the average is 7.1%), and the average false positive rate was 11.8%. The software is available in open source form.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Abdul-Karim, M. A. (2005). Automated parameter selection for segmentation of tube-like biological structures using optimization algorithm and MDL. 2005 Ph.D. dissertation, Rensselaer Polytechnic Institute, Troy, NY 12180.

  • Abdul-Karim, M. A., Al-Kofahi, K., Brown, E. B., Jain, R. K., & Roysam, B. (2003). Automated tracing and change analysis of angiogenic vasculature from in vivo multiphoton confocal image time series. Microvascular Research, 66(2), 113–125.

    Article  PubMed  Google Scholar 

  • Abdul-Karim, M. A., Roysam, B., Dowell-Mesfin, N. M., Jeromin, A., Yuksel, M., & Kalyanaraman, S. (2005). Automatic selection of parameters for vessel/neurite segmentation algorithms. IEEE Transactions on Image Processing, 14(9), 1338–1350.

    Article  PubMed  Google Scholar 

  • Al-Kofahi, K. A., Can, A., Lasek, S., Szarowski, D. H., Dowell-Mesfin, N., Shain, W., et al. (2003). Median-based robust algorithms for tracing neurons from noisy confocal microscope images. IEEE Transactions on Information Technology in Biomedicine, 7(4), 302–317.

    Article  PubMed  Google Scholar 

  • Al-Kofahi, Y., Dowell-Mesfin, N., Pace, C., Shain, W., Turner, J. N., & Roysam, B. (2008). Improved detection of branching points in algorithms for automated neuron tracing from 3-D confocal images. Cytometry A, 73(1), 36–43.

    PubMed  Google Scholar 

  • Al-Kofahi, K. A., Lasek, S., Szarowski, D. H., Pace, C. J., Nagy, G., Turner, J. N., et al. (2002). Rapid automated three-dimensional tracing of neurons from confocal image stacks. IEEE Transactions on Information Technology in Biomedicine, 6(2), 171–187.

    Article  PubMed  Google Scholar 

  • Bai, W., Zhou, X., Ji, L., Cheng, J., & Wong, S. T. (2007). Automatic dendritic spine analysis in two-photon laser scanning microscopy images. Cytometry A, 71(10), 818–826.

    PubMed  Google Scholar 

  • Barron, A., Rissanen, J., & Yu, B. (1998). The minimum description length principle in coding and modeling. IEEE Transactions on Information Theory, 44(6), 2743–2760.

    Article  Google Scholar 

  • Blake, A., & Zisserman, A. (1987). Visual reconstruction. Cambridge: MIT.

    Google Scholar 

  • Bondy, J. A., & Murty, U. S. R. (1976). Graph theory with applications. New York: Elsevier Science.

    Google Scholar 

  • Bouman, C., & Sauer, K. (1993). A generalized Gaussian image model for edge-preserving MAP estimation. IEEE Transactions on Image Processing, 2(3), 296–310.

    Article  CAS  PubMed  Google Scholar 

  • Cai, H., Xu, X., Lu, J., Lichtman, J. W., Yung, S. P., & Wong, S. T. (2006). Repulsive force based snake model to segment and track neuronal axons in 3-D microscopy image stacks. Neuroimage, 32(4), 1608–1620.

    Article  PubMed  Google Scholar 

  • Cajal, S. R. Y. (1888). Estructura de los centros nervioso de las aves. Rev Trim Hitol norm Pat, 1, 1–10.

    Google Scholar 

  • Cajal, S. R. Y. (1891). Sur la structure de l’ecorce cerebrale de quelques mammiferes. Cellule, 7, 123–176.

    Google Scholar 

  • Can, A., Shen, H., Turner, J. N., Tanenbaum, H. L., & Roysam, B. (1999). Rapid automated tracing and feature extraction from live high-resolution retinal fundus images using direct exploratory algorithms. IEEE Transactions on Information Technology in Biomedicine, 3(2), 125–138.

    Article  CAS  PubMed  Google Scholar 

  • Capowski, J. J. (ed). (1989). Computer techniques in neuroanatomy. New York: Plenum.

    Google Scholar 

  • Carlsson, K., Wallen, P., & Brodin, L. (1989). Three-dimensional imaging of neurons by confocal fluorescence microscopy. Journal de Microscopie, 155(Pt 1), 15–26.

    CAS  Google Scholar 

  • Cesar, R. M., Jr., & Costa, L. F. (1999). Semi-automated dendrogram generation for neural shape analysis. Journal of Neuroscience Methods, 93, 121–131.

    Article  PubMed  Google Scholar 

  • Cham, T. J., & Cipolla, R. (1999). Automated B-Spline curve representation incorporating MDL and error-minimizing control point insertion strategies. IEEE Transactions on Pattern analysis and Machine Intelligence, 21(1), 49–53.

    Article  Google Scholar 

  • Cheng, J., Zhou, X., Miller, E., Witt, R. M., Zhu, J., Sabatini, B. L., et al. (2007a). A novel computational approach for automatic dendrite spines detection in two-photon laser scan microscopy. Journal of Neuroscience Methods, 165(1), 122–134.

    Article  Google Scholar 

  • Cheng, J., Zhou, X., Sabatini, B. L., & Wong, S. T. (2007b). NeuronIQ: a novel computational approach for automatic dendrite splines detection and analysis. IEEE/NIH Life Science Systems and Applications Workshop (LISSA 2007) pp. 168–71.

  • Cohen, A. R., Roysam, B., & Turner, J. N. (1994). Automated tracing and volume measurements of neurons from 3-D confocal fluorescence microscopy data. Journal de Microscopie, 173(Pt 2), 103–114.

    CAS  Google Scholar 

  • Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2001). Introduction to algorithms (2nd ed., pp. 595–601). Cambridge: MIT.

    Google Scholar 

  • Cornea, N. D., Silver, D., Yuan, X., & Balasubramanian, R. (2005). Computing hierarchical curve-skeletons of 3-D objects. The Visual Computer, 21(11), 945–955.

    Article  Google Scholar 

  • Costa, L. Da F., Manoel, E. T., Faucereau, F., Chelly, J., van Pelt, J., & Ramakers, G. (2002). Shape analysis framework for neuromorphometry. Network, 13(3), 283–310.

    Article  Google Scholar 

  • Dierckx, P. (1993). Curve and surface fitting with splines. Oxford: Clarendon.

    Google Scholar 

  • Dowell-Mesfin, N. M., Abdul-Karim, M. A., Turner, A. M., Schanz, S., Craighead, H. G., Roysam, B., et al. (2004). Topographically modified surfaces affect orientation and growth of hippocampal neurons. Journal of Neural Engineering, 1(2), 78–90.

    Article  CAS  PubMed  Google Scholar 

  • Falcao, A. X., Costa, L. F., & da Cunha, B. S. (2002). Multiscale skeletons by image foresting transform and its application to neuromorphometry. Pattern Recognition, 35(7), 1571–1582.

    Article  Google Scholar 

  • Frangi, A. F., Niessen, W. J., Hoogeveen, R. M., Walsum, T. V., & Viergever, M. A. (1999). Model-based quantitation of 3-D magnetic resonance angiographic images. IEEE Transactions on Medical Imaging, 18(10), 946–956.

    Article  CAS  PubMed  Google Scholar 

  • Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998). Multiscale vessel enhancement filtering. Medical Image Computing and Computer-Assisted Intervention, 1496, 130–137.

    Google Scholar 

  • Glaser, E. M., Tagamets, M., McMullen, N. T., & Van der Loos, H. (1983). The image-combining computer microscope—an interactive instrument for morphometry of the nervous system. Journal of Neuroscience Methods, 8(1), 17–32.

    Article  CAS  PubMed  Google Scholar 

  • Globus, A., Levit, C., & Lasinski, T. (1991) A tool for visualizing the topology of three-dimensional vector fields. IEEE Visualization, 33–40

  • Grunwald, P., Myung, J., & Pitt, M. (2004). Advances in minimum description length: Theory and applications. Cambridge: MIT Press.

    Google Scholar 

  • Guéziec, A., & Ayache, N. (1994). Smoothing and matching of 3-D space curves. International Journal of Computer Vision, 12(1), 79–104.

    Article  Google Scholar 

  • Gulledge, A. T., Kampa, B. M., & Stuart, G. J. (2005). Synaptic integration in dendritic trees. Journal of Neurobiology, 64, 75–90.

    Article  CAS  PubMed  Google Scholar 

  • He, W., Hamilton, T. A., Cohen, A. R., Holmes, T. J., Pace, C., Szarowski, D. H., et al. (2003). Automated three-dimensional tracing of neurons in confocal and brightfield images. Microscopy and Microanalysis, 9(4), 296–310.

    Article  CAS  PubMed  Google Scholar 

  • Herzog, A., Krell, G., Michaelis, B., Wang, J., Zuschratter, W., & Braun, K. (1997). Restoration of three-dimensional quasi-binary images from confocal microscopy and its application to dendritic trees. BiOS. San Jose, 8–14.

  • Holmes, T. J., Bhattacharyya, S., Cooper, J. A., Hanzel, D., Krishnamurthi, V., Lin, W., et al. (1995). Light microscopic images reconstructed by maximum likelihood deconvolution. In J. Pawley (Ed.), Handbook of confocal microscopy. New York: Plenum.

    Google Scholar 

  • Janoos, F., Nouansengsy, B., Xu, X., MacHiraju, R., & Wong, S. T. C. (2008). Classification and uncertainty visualization of dendritic spines from optical microscopy imaging. Computer Graphics Forum, 27(3), 879–886.

    Article  Google Scholar 

  • Kalus, P., Muller, T. J., Zuschratter, W., & Senitz, D. (2000). The dendritic architecture of prefrontal pyramidal neurons in schizophrenic patients. NeuroReport, 11(16), 3621–3625.

    Article  CAS  PubMed  Google Scholar 

  • Kirbas, C., & Quek, F. (2004). A review of vessel extraction techniques and algorithms. ACM Computing Surveys, 36(2), 81–121.

    Article  Google Scholar 

  • Koh, I. Y., Lindquist, W. B., Zito, K., Nimchinsky, E. A., & Svoboda, K. (2002). An image analysis algorithm for dendritic spines. Neural Computation, 14(6), 1283–1310.

    Article  PubMed  Google Scholar 

  • Leclerc, Y. G. (1989). Constructing simple stable descriptions for image partitioning. International Journal of Computer Vision, 3(1), 73–102.

    Article  Google Scholar 

  • Lippman, J., & Dunaevsky, A. (2005). Dendritic spine morphogenesis and plasticity. Journal of Neurobiology, 64(1), 47–57.

    Google Scholar 

  • Lolive, D., Barbot, N., & Boeffard, O. (2006). Melodic contour estimation with B-spline models using a MDL criterion. Proceedings of the 11th International Conference on Speech and Computer (SPECOM) (pp. 333-338). Saint Petersburg, Russia

  • London, M., & Hausser, M. (2005). Dendritic computation. Annual Review of Neuroscience, 28, 503–532.

    Article  CAS  PubMed  Google Scholar 

  • Losavio, B. E., Liang, Y., Santamaria-Pang, A., Kakadiaris, I. A., Colbert, C. M., & Saggau, P. (2008). Live neuron morphology automatically reconstructed from multiphoton and confocal imaging data. Journal of Neurophysiology, 100, 2422–2429.

    Article  PubMed  Google Scholar 

  • Lu, F., & Milios, E. (1994). Optimal spline fitting to planar shape. Signal Processing, 37, 129–140.

    Article  Google Scholar 

  • Matsuzaki, M. (2007). Factors critical for the plasticity of dendritic spines and memory storage. Neuroscience Research, 57, 1–9.

    Article  PubMed  Google Scholar 

  • Meijering, E., Jacob, M., Sarria, J. C., Steiner, Pl, Hirling, H., & Unser, M. (2004). Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images. Cytometry, 58A(2), 167–176.

    Article  Google Scholar 

  • Mel, B. W. (1994). Information-processing in dendritic trees. Neural Computation, 6, 1031–1085.

    Article  Google Scholar 

  • Miller, M. I., Roysam, B., Smith, K. R., & O'Sullivan, J. A. (1991). Representing and computing regular languages on massively parallel networks. IEEE Transactions on Neural Networks, 2(1), 56–72.

    Article  CAS  PubMed  Google Scholar 

  • Pawley, J. B. (2006). Handbook of biological confocal microscopy (3rd ed.) Springer.

  • Perona, P., & Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7), 629–639.

    Article  Google Scholar 

  • Potter, S. M. (1996). Vital imaging: two photons are better than one. Current Biology, 6(12), 1595–1598.

    Article  CAS  PubMed  Google Scholar 

  • Potter, S. M. (2005). Two-photon microscopy for 4D imaging of living neurons. In R. Yuste & A. Konnerth (Eds.), Imaging in neuroscience and development: A laboratory manual (pp. 59–70). Cold Spring Harbor: Cold Spring Harbor Laboratory.

    Google Scholar 

  • Rissanen, J. (1978). Modeling by shortest data description. Automatica, 14(5), 465–471.

    Article  Google Scholar 

  • Rodriguez, A., Ehlenberger, D. B., Hof, P. R., & Wearne, S. L. (2006). Rayburst sampling. An algorithm for automated three-dimensional shape analysis from laser scanning microscopy images. National Protocol, 1(4), 2152–2161.

    Article  CAS  Google Scholar 

  • Rodriguez, A., Ehlenberger, D., Kelliher, K., Einstein, M., Henderson, S. C., Morrison, J. H., et al. (2003). Automated reconstruction of three-dimensional neuronal morphology from laser scanning microscopy images. Methods, 30(1), 94–105.

    Article  CAS  PubMed  Google Scholar 

  • Rogers, D. F. (1998). Procedural elements for computer graphics. Boston: McGraw-Hill.

    Google Scholar 

  • Rolston, J. D., Wagenaar, D. A., & Potter, S. M. (2007). Precisely timed spatiotemporal patterns of neural activity in dissociated cortical cultures. Neuroscience, 148(1), 294–303.

    Article  CAS  PubMed  Google Scholar 

  • Satou, K., Aoki, Y., Mataga, N., Hensh, T. K., & Taki, K. (2005). Automatic analysis for neuron by confocal laser scanning microscope. Optomechatronic Machine Vision, Proceedings of SPIE, 6051.

  • Schmitt, S., Evers, J. F., Duch, C., Scholz, M., & Obermayer, K. (2004). New methods for the computer-assisted 3-D reconstruction of neurons from confocal image stacks. Neuroimage, 23(4), 1283–1298.

    Article  PubMed  Google Scholar 

  • Schroeder, W., Martin, K., & Lorensen, B. (1998). The visualization toolkit: An object oriented approach to 3-D graphics. Printice-Hall Inc.

  • Schumaker, L. L. (1981). Spine functions: Basic theory. New York: Wiley.

    Google Scholar 

  • Scorcioni, R., Polavaram, S., & Ascoli, G. A. (2008). L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nature Protocols, 3(5), 866–876.

    Google Scholar 

  • Srinivasan, R., Zhou, X., Miller, E., Lu, J., Litchman, J., & Wong, S. T. (2007). Automated axon tracking of 3-D confocal laser scanning microscopy images using guided probabilistic region merging. Neuroinformatics, 5(3), 189–203.

    Article  PubMed  Google Scholar 

  • Theisel, H., & Weinkauf, T. (2002). Vector field metrics based on distance measures of first order critical points. Journal of WSCG, 10(3).

  • Trachtenberg, J. T., Chen, B. E., Knott, G. W., Feng, G., Sanes, J. R., Welker, E., et al. (2002). Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex. Nature, 420(6917), 788–794.

    Article  CAS  PubMed  Google Scholar 

  • Turner, J. N., Szarowski, D. H., Smith, K. L., Marko, M., Leith, A., & Swann, J. W. (1991). Confocal microscopy and three-dimensional reconstruction of electrophysiologically identified neurons in thick brain slices. Journal of Electron Microscopy Technique, 18(1), 11–23.

    Article  CAS  PubMed  Google Scholar 

  • Turner, J. N., Szarowski, D. H., Turner, T. J., Ancin, H., Lin, W. C., Roysam, B., et al. (1994). Three-dimensional imaging and image analysis of hippocampal neurons: confocal and digitally enhanced wide field microscopy. Microscopy Research and Technique, 29(4), 269–278.

    Article  CAS  PubMed  Google Scholar 

  • Tyrrell, J. A., di Tomaso, E., Fuja, D., Tong, R., Kozak, K., Jain, R. K., et al. (2007). Robust 3-D modeling of vasculature imagery using superellipsoids. IEEE Transactions on Medical Imaging, 26(2), 223–237.

    Article  PubMed  Google Scholar 

  • Tyrrell, J. A., Mahadevan, V., Tong, R. T., Brown, E. B., Jain, R. K., & Roysam, B. (2005). A 2-D/3-D model-based method to quantify the complexity of microvasculature imaged by in vivo multiphoton microscopy. Microvascular Research, 70(3), 165–178.

    Article  PubMed  Google Scholar 

  • Vliet, L. J. V. (1993). Grey-scale measurements in multi-dimensional digitized images. Ph.D. Thesis of the Pattern Recognition Group, Delft University of Technology, The Netherlands.

  • Wearne, S. L., Rodriguez, A., Ehlenberger, D. B., Rocher, A. B., Henderson, S. C., & Hof, P. R. (2005). New techniques for imaging, digitization and analysis of three-dimensional neural morphology on multiple scales. Neuroscience, 136(3), 661–680.

    Article  CAS  PubMed  Google Scholar 

  • Weaver, C. M., Hof, P. R., Wearne, S. L., & Lindquist, W. B. (2004). Automated algorithms for multiscale morphometry of neuronal dendrites. Neural Computation, 16(7), 1353–1383.

    Article  PubMed  Google Scholar 

  • Wu, C. C., Reilly, J. F., Young, W. G., Morrison, J. H., & Bloom, F. E. (2004). High-throughput morphometric analysis of individual neurons. Cerebral Cortex, 14(5), 543–554.

    Article  PubMed  Google Scholar 

  • Xiong, G., Zhou, X., Degterev, A., Ji, L., & Wong, S. T. (2006). Automated neurite labeling and analysis in fluorescence microscopy images. Cytometry A, 69(6), 494–505.

    PubMed  Google Scholar 

  • Xu, X., & Wong, S. T. (2006). Optical microscopic image processing of dendritic spines morphology. IEEE Signal Processing Magazine, 23(4), 132–135.

    Article  Google Scholar 

  • Yu, Z., & Bajaj, C. (2004). A segmentation-free approach for skeletonization of gray-scale images via anisotropic vector diffusion. Computer Vision and Pattern Recognition, CVPR. Proceedings of the 2004 IEEE Computer Society Conference on, 1, 415–20.

  • Yuste, R., & Bonhoeffer, T. (2001). Morphological changes in dendritic spines associated with long-term synaptic plasticity. Annual Review of Neuroscience, 24, 1071–1089.

    Article  CAS  PubMed  Google Scholar 

  • Zhang, Y., Zhou, X., Degterev, A., Lipinski, M., Adjeroh, D., Yuan, J., et al. (2007a). A novel tracing algorithm for high throughput imaging screening of neuron-based assays. Journal of Neuroscience Methods, 160(1), 149–162.

    Article  PubMed  Google Scholar 

  • Zhang, Y., Zhou, X., Witt, R. M., Sabatini, B. L., Adjeroh, D., & Wong, S. T. (2007b). Automated spine detection using curvilinear structure detector and LDA Classifier. Biomedical Imaging: From Nano to Macro, ISBI. 4th IEEE International Symposium on, pp. 528–531.

  • Zhang, Y., Zhou, X., Witt, R. M., Sabatini, B. L., Adjeroh, D., & Wong, S. T. (2007c). Dendritic spine detection using curvilinear structure detector and LDA classifier. Neuroimage, 36(2), 346–360.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The image analysis aspects of this work were supported by NIH Biomedical Research Partnerships Grant R01 EB005157, by the Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (Award Number EEC-9986821), and by Rensselaer Polytechnic Institute. The Potter laboratory images were collected by SMP and David Kantor in collaboration with Erin Schuman and Scott Fraser. Trachtenberg lab work was supported by NIMH grant P50 MH077972.

Author information

Authors and Affiliations

  1. Jonsson Engineering Center, Center for Subsurface Sensing & Imaging Systems, Rensselaer Polytechnic Institute, Rm 7010, Troy, NY, 12180, USA

    Xiaosong Yuan & Badrinath Roysam

  2. Department of Neurobiology, David Geffen School of Medicine, Los Angeles, CA, 90095, USA

    Joshua T. Trachtenberg

  3. Laboratory for Neuroengineering, Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Steve M. Potter

Authors
  1. Xiaosong Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joshua T. Trachtenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Steve M. Potter
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Badrinath Roysam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Badrinath Roysam.

Electronic supplementary materials

Below is the link to the electronic supplementary material.

ESM 1

(ZIP 1.25 mb)

ESM 2

(PPT 6.15 mb)

ESM 3

Table of adjustable parameters used in the software, and their descriptions (DOC 127 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yuan, X., Trachtenberg, J.T., Potter, S.M. et al. MDL Constrained 3-D Grayscale Skeletonization Algorithm for Automated Extraction of Dendrites and Spines from Fluorescence Confocal Images. Neuroinform 7, 213–232 (2009). https://doi.org/10.1007/s12021-009-9057-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-009-9057-y

Keywords

Search

Navigation