Skip to main content

Advertisement

SpringerLink
Log in

Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using 3D Tubular Models

  • Original Article
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

The challenges faced in analyzing optical imaging data from neurons include a low signal-to-noise ratio of the acquired images and the multiscale nature of the tubular structures that range in size from hundreds of microns to hundreds of nanometers. In this paper, we address these challenges and present a computational framework for an automatic, three-dimensional (3D) morphological reconstruction of live nerve cells. The key aspects of this approach are: (i) detection of neuronal dendrites through learning 3D tubular models, and (ii) skeletonization by a new algorithm using a morphology-guided deformable model for extracting the dendritic centerline. To represent the neuron morphology, we introduce a novel representation, the Minimum Shape-Cost (MSC) Tree that approximates the dendrite centerline with sub-voxel accuracy and demonstrate the uniqueness of such a shape representation as well as its computational efficiency. We present extensive quantitative and qualitative results that demonstrate the accuracy and robustness of our method.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

Notes

  1. Loops are not allowed in a tree structure.

  2. The coordinates of the points in the soma-pipette region are used to obtain the center of the ellipse given by the mean of the points and the semi-axis of the ellipse given by the covariance matrix.

  3. The solution of Eq. (4) coincides with the centerline of each dendritic branch since small distances of a voxel from V D are penalized and the only points that are equidistant from the dendritic boundary are those of the centerline.

References

  • Bas, E., & Erdogmus, D. (2011). Principal curves as skeletons of tubular objects. Neuroinformatics, 9(2–3), 181–191.

    Article  PubMed  Google Scholar 

  • Basu, S., Ooi, W.T., Racoceanu, D. (2014). Improved marked point process priors for single neurite tracing. In IEEE international workshop on Pattern recognition in neuroimaging (pp. 1–4).

  • Breitenreicher, D., Sofka, M., Britzen, S., Zhou, S. (2013). Hierarchical discriminative framework for detecting tubular structures in 3D images. In J. Gee (Ed.), Proceedings of information processing in medical imaging,Lecture Notes in computer Science (Vol. 7917, pp. 328–339). Asilomar, CA: Springer-Verlag Berlin Heidelberg.

    Chapter  Google Scholar 

  • Broser, P., Schulte, R., Roth, A., Helmchen, F., Lang, S., Wittum, G., Sakmann, B. (2004). Nonlinear anisotropic diffusion filtering of three-dimensional image data from two-photon microscopy. Journal of Biomedical Optics, 9(6), 1253–1264.

    Article  PubMed  Google Scholar 

  • Brown, K., Barrionuevo, G., Canty, A., Paola, V., Hirsch, J., Jefferis, G., Lu, J., Snippe, M., Sugihara, I., Ascoli, G. (2011). The DIADEM data sets: representative light microscopy images of neuronal morphology to advance automation of digital reconstructions. Neuroinformatics, 9(2-3), 143–157.

    Article  PubMed Central  PubMed  Google Scholar 

  • Chothani, P., Mehta, V., Stepanyants, A. (2011). Automated tracing of neurites from light microscopy stacks of images. Neuroinformatics, 9(2–3), 263–278.

    Article  PubMed Central  PubMed  Google Scholar 

  • Computational Biomedicine Lab (2014). ORION: Online Reconstruction and functional Imaging Of Neurons. http://www.cbl.uh.edu/ORION.

  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

    Google Scholar 

  • Cuntz, H., Forstner, F., Borst, A., Häusser, M., Morrison, A. (2010). One rule to grow them all: A general theory of neuronal branching and its practical application. PLoS Computational Biology, 6(8).

  • Diestel, R. (2005). Graph theory. Berlin Heidelberg New York: Springer.

    Google Scholar 

  • Dijkstra, E. (1959). A note on two problems in connection with graphs. Numerische Mathematic, 1, 269–271.

    Article  Google Scholar 

  • Dima, A., Scholz, M., Obermayer, K. (2002). Automatic segmentation and skeletonization of neurons from confocal microscopy images based on the 3-D wavelet transform. IEEE Transactions on Image Processing, 11(7), 790–801.

    Article  PubMed  Google Scholar 

  • Duke/Southampton (2013). Neuroscience research group, school of biological sciences, southampton university: Duke/southampton archive of neuronal morphology. http://neuron.duke.edu/cells/.

  • Evers, J., Schmitt, S., Sibila, M., Duch, C. (2005). Progress in functional neuroanatomy: Precise automatic geometric reconstruction of neuronal morphology from confocal image stacks. Journal of Neurophysiology, 93(4), 2331–2342.

    Article  CAS  PubMed  Google Scholar 

  • Frangi, A., Niessen, W., Vincken, K., Viergever, M. (1998). Multiscale vessel enhancement filtering. In Proceedings of medical image computing and computer assisted intervention, (Vol. 1496 pp. 130–137). Cambridge, MA.

  • Gillette, T., Brown, K., Ascoli, A. (2011). The DIADEM metric: comparing multiple reconstructions of the same neuron. Neuroinformatics, 9(2–3), 233–245.

    Article  PubMed Central  PubMed  Google Scholar 

  • Gonzalez, G., Fleuret, F., Fua, P. (2009). Learning rotational features for filament detection. In Proceedings of IEEE computer society conference on computer vision and pattern recognition (pp. 1582–1589). Miami Beach, FL.

  • González, G., Turetken, E., Fleuret, F., Fua, P. (2010). Delineating trees in noisy 2D images and 3D image-stacks. In Proceedings of IEEE computer society conference on computer vision and pattern recognition (pp. 2799–2806). San Francisco, CA.

  • Greenspan, H., Laifenfeld, M., Einav, S., Barnea, O. (2001). Evaluation of center-line extraction algorithms in quantitative coronary angiography. IEEE Transactions on Medical Imaging, 20(9), 928–941.

    Article  CAS  PubMed  Google Scholar 

  • Hassouna, M., Farag, A., Falk, R. (2005). Differential Fly-Throughs (DFT): A general framework for computing flight paths. In Proceedings of medical image computing and computer-assisted intervention, (Vol. 1 pp. 654–661). PalmSprings, CA

  • Kakadiaris, I., Santamaría-Pang, A., Colbert, C., Saggau, P. (2008). Automatic 3-D morphological reconstruction of neuron cells from multiphoton images. In J. Rittscher, R. Machiraju, S. Wong (Eds.), Microscopic image analysis for life science applications, (pp. 389–399). Norwood, MA: Artech House.

    Google Scholar 

  • Lorenz, C., Carlsen, I., Buzug, T., Fassnacht, C., Weese, J. (1997). Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images. In Proceedings of first joint conference on computer vision, virtual reality and robotics in medicine and medial robotics and computer-assisted surgery, (Vol. 1205 pp. 233–244).

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

    Article  PubMed  Google Scholar 

  • Mayerich, D., Bjornsson, C., Taylor, J., Roysam, B. (2012). NetMets: software for quantifying and visualizing errors in biological network segmentation. BMC bioinformatics, 13 (Suppl 8), S7.

    PubMed Central  PubMed  Google Scholar 

  • Meijering, E. (2010). Neuron tracing in perspective. Cytometry Part A, 77A(7), 693–704.

    Article  Google Scholar 

  • Ming, X., Li, A., Wu, J., Yan, C., Ding, W., Gong, H., Zeng, S., Liu, Q. (2013). Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling. PloS one, 8(12), e84,557.

    Article  Google Scholar 

  • Mizrahi, A., Ben-Ner, E., Katz, M., Kedem, K., Glusman, J., Libersat, F. (2000). Comparative analysis of dendritic architecture of identified neurons using the Hausdorff distance metric. The Journal of Comparative Neurology, 233(3), 415–428.

    Article  Google Scholar 

  • Neurolucida (2014). MBF Bioscience: stereology and neuron morphology quantitative analysis. http://www.mbfbioscience.com.

  • Pawley, J. (2006). Handbook of Biological Confocal Microscopy. New York: Springer-Verlag.

    Book  Google Scholar 

  • Pelt, J.v., & Schierwagen, A. (2004). Morphological analysis and modeling of neuronal dendrites. Mathematical Biosciences, 188(1-2), 147–155.

    Article  PubMed  Google Scholar 

  • Peng, H., Long, F., Myers, G. (2011). Automatic 3D neuron tracing using all-path pruning. Bioinformatics, 27(13), i239.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • 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 

  • Platt, J.C. (2000). Probabilistic outputs for support vector machines and comparison to regularize likelihood methods. Advances in large margin classifiers, (pp. 61–74).

  • Rall, W. (1977). Handbook of Physiology: The Nervous System. Chapter Core conductor theory and cable properties of neurons (Vol. 1, pp. 39–98). Baltimore, MD: American Physiological Society Bethesda.

  • Rosenfeld, A., & Pfaltz, J. (1968). Distance functions on digital pictures. Pattern Recognition, 1(1), 33–61.

    Article  Google Scholar 

  • Rouchdy, Y., & Cohen, L. (2009). The shading zone problem in geodesic voting and its solutions for the segmentation of tree structures. application to the segmentation of microglia extensions. In Proceedings of computer vision and pattern recognition workshops (pp. 66–71). Miami

  • Santamaría-Pang, A., Bildea, T., Colbert, C., Saggau, P., Kakadiaris, I. (2006). Towards segmentation of irregular tubular structures in 3D confocal microscope images. In Proceedings of miccai workshop in microscopic image analysis and applications in biology (pp. 78–85). Denmark, Copenhagen.

  • Santamaría-Pang, A., Colbert, C., Losavio, B., Saggau, P., Kakadiaris, I. (2007a). Automatic morphological reconstruction of neurons from optical images. In Proceedings of international workshop in microscopic image analysis and applications in biology. Piscataway.

  • Santamaría-Pang, A., Colbert, C., Saggau, P., Kakadiaris, I. (2007b). Automatic centerline extraction of irregular tubular structures using probability volumes from multiphoton imaging. In Proceedings of medical image computing and computer-assisted intervention (pp. 486–494). Brisbane

  • Santamaría-Pang, A., Bildea, T., Tan, S., Kakadiaris, I. (2008). Denoising for 3-D photon-limited imaging data using nonseparable filterbanks. IEEE Transactions on Image Processing, 17(12), 2312–2323.

    Article  PubMed Central  PubMed  Google Scholar 

  • Sato, Y., Nakajima, S., Atsumi, H., Koller, T., Gerig, G., Yoshida, S., Kikinis, R. (1998). 3-D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical Image Analysis, 2(2), 143–168.

  • Sethian, J. (1996). Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision and Materials Sciences. Cambridge: Cambridge University Press.

    Google Scholar 

  • Sholl, D. (1953). Dendritic organization in the neurons of the visual and motor cortices of the cat. Journal of Anatomy, 87(4), 387–406.

    CAS  PubMed Central  PubMed  Google Scholar 

  • Srinivasan, R., Li, Q., Zhou, X., Lu, J., Lichtman, J., Wong, S. (2010). Reconstruction of the neuromuscular junction connectome. Bioinformatics, 26(12), i64–i70.

    Article  Google Scholar 

  • Turetken, E., Benmansour, F., Fua, P. (2012). Automated reconstruction of tree structures using path classifiers and mixed integer programming. In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR) (pp. 566–573). Rhode Island.

  • Turetken, E., Benmansour, F., Andres, B., Pfister, H., Fua, P. (2013). Reconstructing loopy curvilinear structures using integer programming. In Proceedings of the IEEE, CVPR (pp. 1822–1829). Portland.

  • Uehara, C., Colbert, C.M., Saggau, P., Kakadiaris, I. (2004). Towards automatic reconstruction of dendrite morphology from live neurons. In Proceedings of 26th annual international conference of the IEEE engineering in medicine and biology society (pp. 1798–1801). San Fransisco.

  • Urban, S., O’Malley, S.M., Walsh, B., Santamaria-Pang, A., Saggau, P., Colbert, C., Kakadiaris, I. (2006). Automatic reconstruction of dendrite morphology from optical section stacks. In Proceedings of 2nd international workshop on computer vision approaches to medical image analysis (pp. 190–201). Graz: Springer.

  • Uylings, H., & van Pelt, J. (2002). Measures for quantifying dendritic arborizations. Network: Computation in Neural Systems, 13(3), 397–414.

    Article  Google Scholar 

  • Vapnik, V. (1995). The Nature of Statistical Learning Theory. Berlin: Springer-Verlag.

    Book  Google Scholar 

  • Vasilkoski, Z., & Stepanyants, A. (2009). Detection of the optimal neuron traces in confocal microscopy images. Journal of Neuroscience Methods, 178(1), 197–204.

    Article  PubMed Central  PubMed  Google Scholar 

  • Wang, Y., Narayanaswamy, A., Roysam, B. (2011a). Novel 4-D open-curve active contour and curve completion approach for automated tree structure extraction. In Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE (pp. 1105–1112). Colorado Springs.

  • Wang, Y., Narayanaswamy, A., Tsai, C.-L., Roysam, B. (2011b). A broadly applicable 3-D neuron tracing method based on open-curve snake. Neuroinformatics, 9(2-3), 193–217.

    Article  PubMed  Google Scholar 

  • Wearne, S., Rodriguez, A., Ehlenberger, D., Rocher, A., Henderson, S., Hof, P. (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 

  • Xiao, H., & Peng, H. (2013). APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree. Bioinformatics, 29(11), 1448– 1454.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Xie, J., Zhao, T., Lee, T., Myers, E., Peng, H. (2010). Automatic neuron tracing in volumetric microscopy images with anisotropic path Searching. In Proceedings of the medical image computing and computer-assisted intervention (pp. 472–479). Beijing.

  • Xie, J., Zhao, T., Lee, T., Myers, E., Peng, H. (2011). Anisotropic path searching for automatic neuron reconstruction. Medical image analysis, 15(5), 680–689.

    Article  PubMed  Google Scholar 

  • Yuan, X., Trachtenberg, J., Potter, S., Roysam, B. (2009). MDL constrained 3-D grayscale skeletonization algorithm for automated extraction of dendrites and spines from fluorescence confocal images. Neuroinformatics, 7, 213–232.

    Article  PubMed Central  PubMed  Google Scholar 

  • Zhao, T., Xie, J., Amat, F., Clack, F., Ahammad, P., Peng, H., Long, F., Myers, E. (2011). Automated reconstruction of neuronal morphology based on local geometrical and global structural models. Neuroinformatics, 9(2-3), 247–261.

    Article  PubMed Central  PubMed  Google Scholar 

  • Zhao, Y., Xiong, H., Zhang, K., Zhou, X. (2009). Equilibrium modeling for 3D curvilinear structure tracking of confocal microscopy images. In Proceedings of the IEEE international conference on image processing (pp. 2533–2536). Cairo.

Download references

Acknowledgments

We wish to thank all of the members of the ORION team (Computational Biomedicine Lab 2014) and especially Costa M. Colbert, Yong Liang, and Bradley E. Losavio. The data were acquired at P. Saggau’s Laboratory in the Department of Neuroscience of the Baylor College of Medicine. This work was supported in part by NIH 5R01EB001048-02, NSF-DMS 0915242, NHARP 003652-0136-2009 and the University of Houston–Eckhard Pfeiffer Endowment Fund. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and may not reflect the views of UH, NIH, NHARP, or NSF.

Author information

Authors and Affiliations

  1. Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, TX, 77204, USA

    Alberto Santamaría-Pang, Paul Hernandez-Herrera & Manos Papadakis

  2. Department of Mathematics, University of Houston, Houston, TX, 77204, USA

    Peter Saggau

  3. Department of Neuroscience, Baylor College of Medicine, Houston, TX, 77030, USA

    Ioannis A. Kakadiaris

Authors
  1. Alberto Santamaría-Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul Hernandez-Herrera
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manos Papadakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peter Saggau
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ioannis A. Kakadiaris
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ioannis A. Kakadiaris.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Santamaría-Pang, A., Hernandez-Herrera, P., Papadakis, M. et al. Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using 3D Tubular Models. Neuroinform 13, 297–320 (2015). https://doi.org/10.1007/s12021-014-9253-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-014-9253-2

Keywords

Search

Navigation