Abstract
Centerline tracing in dendritic structures acquired from confocal images of neurons is an essential tool for the construction of geometrical representations of a neuronal network from its coarse scale up to its fine scale structures. In this paper, we propose an algorithm for centerline extraction that is both highly accurate and computationally efficient. The main novelties of the proposed method are (1) the use of a small set of Multiscale Isotropic Laplacian filters, acting as self-steerable filters, for a quick and efficient binary segmentation of dendritic arbors and axons; (2) an automated centerline seed points detection method based on the application of a simple 3D finite-length filter. The performance of this algorithm, which is validated on data from the DIADEM set appears to be very competitive when compared with other state-of-the-art algorithms.
















Similar content being viewed by others
Notes
The roof over the symbol of the filter denotes its Fourier transform.
These properties imply that the filter ϕ and all of its derivatives up to second order are well-localized in space. This means that, for practical purposes, the spatial support of ϕ and of its derivatives up to second order is small.
This quantity represents that ratio of the length in the z-direction of a voxel relative to its length in the x,y directions. Therefore, it characterizes the sampling grid and thus it shows the anisotropy of the point-spread function in the z-direction.
References
3D-Slicer (2008). http://www.slicer.org/.
Al-Kofahi, K., Lasek, S., Szarowski, D., Pace, C., Nagy, G. (2002). Rapid automated three-dimensional tracing of neurons from confocal image stacks. IEEE Trans Information Technology in Biomedicine, 6(2), 171–187.
Bas, E., & Erdogmus, D. (2011). Principal curves as skeletons of tubular objects. Neuroinformatics, 9(2-3), 181–191.
Bodmann, B., Hoffman, D., Kouri, D., Papadakis, M. (2007). Hermite distributed approximating functionals as almost-ideal low-pass filters. Sampling Theory in Image and Signal Processing, 7, 15–38.
Breitenreicher, D., Sofka, M., Britzen, S., Zhou, S. (2013). Hierarchical discriminative framework for detecting tubular structures in 3d images In Gee, J. (Ed.), Proc. Information Processing in Medical Imaging. Lecture Notes in computer Science (Vol. 7917, pp. 328–339). Berlin Heidelberg: Springer-Verlag.
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 (Vol. 9, pp. 143–157).
Chandler, C., & Gibson, A.G. (1999). Uniform approximation of functions with discrete approximation functionals. J Approx Th, 100, 233–250.
Chothani, P., Mehta, V., Stepanyantas, A. (2011). Automated tracing of neurites from light microscopy stacks of images. Neuroinformatics, 9(2-3), 263–278.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273–297.
Dijkstra, E. (1959). A note on two problems in connection with graphs. Numerische Mathematic, 1, 269–271.
Donohue, D., & Ascoli, G. (2010). Automated reconstruction of neuronal morphology: an Overview. Brain Research Reviews.
Easley, G., Labate, D., Lim, W.Q. (2008). Sparse directional image representations using the discrete shearlet transform. Applied and Computational Harmonic Analysis, 25(1), 25–46.
Frangi, A., Niessen, W., Vincken, K., Viergever, M. (1998). Multiscale vessel enhancement filtering. In: Proceedings Medical Image Computing and Computer Assisted Intervention, (Vol. 1496. Cambridge, MA, pp. 130–137).
Hassouna, M., & Farag, A. (2005). Robust centerline extraction framework using level sets. In: IEEE Proceedings Conference on Computer Vision and Pattern Recognition, (Vol. 1, pp. 458–465).
Hines, M., & Carnevale, N. (2001). NEURON: a tool for neuroscientists. The Neuroscientist, 7, 123–135.
Hoffman, D.K., & Nayar, N. (1991). Analytic banded approximation for the discretized free propagator. Journal of Physical Chemistry, 95, 8299–8305.
Hoffman, D.K., Frishman, A.M., Kouri, D.J. (1996). Distributed approximating functional approach to fitting multi-dimensional surfaces. Chemical Physics Letters, 262, 393–399.
Hoffman, D.K., Kouri, D.J., Pollak, E. (2002). Reducing gaussian noise using distributed approximating functionals. Computer Physics Communications, 147, 759–769.
Jiménez, D., Papadakis, M., Labate, D., Kakadiaris, I.A. (2013). Improved automatic centerline tracing for dendritic structures. In: Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on. IEEE, (pp. 1050–1053).
Jimenez, D., & Labate, D. (2014). Papadakis M A directional representation for 3D tubular structures resulting from isotropic well-localized atoms under review.
Kakadiaris, I., & Colbert, C. (2007). Orion: Automated reconstruction of neuronal morphologies from image stacks. In: Proceedings 24th Annual Houston Conference on Biomedical Engineering Research, Houston, (p. 275).
Koh, Y. (2001). Automated recognition algorithms for neural studies. PhD thesis, State University of New York at Stony Brook.
Krissian, K., Malandain, G., Ayache, N., Vaillant, R., Trousset, Y. (2000). Model based detection of tubular structures in 3d images. Computer Vision and Image Understanding, 80(2), 130–171.
Luisi, J., Narayanaswamy, A., Galbreath, Z., Roysam, B. (2011). The FARSIGHT trace editor: an open source tool for 3-D inspection and efficient pattern analysis aided editing of automated neuronal reconstructions. Neuroinformatics, 9(2-3), 305–315.
Meijering, E. (2010). Neuron tracing in perspective. Cytometry Part A, 77A(7), 693–704. doi:10.1002/cyto.a.20895.
Morrison, P., & Zou, J. (2006). Skeletonization based on error reduction. Pattern Recognition, 39(6), 1099–1109.
Negi, P., & Labate, D. (2012). 3D discrete shearlet transform and video processing. IEEE Transactions Image Process, 21(6), 2944–2954. URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6129426.
Neurolucida (2013). MBF Bioscience: stereology and neuron morphology quantitative analysis. URL http://www.mbfbioscience.com.
Ozcan, B., Labate, D., Jiménez, D., Papadakis, M. (2013). Proceedings Wavelets and Sparsity XV. In: D Van De Ville MP V Goyal (Ed.), Vol. 8858. SPIE, San Diego.
Peng, H., Long, F., Myers, G. (2011). Automatic 3D neuron tracing using all-path pruning. Bioinformatics, 27(13), i239.
Rodriguez, A., Ehlenberger, D., Hof, P., Wearne, S. (2009). Three-dimensional neuron tracing by voxel scooping. Journal of Neuroscience Methods, 184(1), 169–175. doi:10.1016/j.jneumeth.2009.07.021.
Santamaria-Pang, A., Colbert, C., Losavio, B., Saggau, P., Kakadiaris, I. (2007). Automatic morphological reconstruction of neurons from optical images. In: Proceedings International Workshop in Microscopic Image Analysis and Applications in Biology. Piscataway, NJ.
Santamaria-Pang, A., Hernandez-Herrera, P., M PPS, Kakadiaris, I.A. (2014). Automatic morphological reconstruction of neurons from multiphoton and confocal microscopy images. Neuroinformatics.
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.
Sato, Y., Westin, C., Bhalerao, A., Nakajima, S., Shiraga, N., Tamura, S., Kikinis, R. (2000). Tissue classification based on 3d local intensity structures for volume rendering. IEEE Transactions on Visualization and Computer Graphics, 6(2), 160–180.
Scorcioni, R.A.G., & Polavaram, S. (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. doi:10.1038/nprot.2008.51.
Turetken, E., Gonzalez, G., Blum, C., Fua, P. (2011). Automated reconstruction of dendritic and axonal trees by global optimization with geometric priors. Neuroinformatics, 9(2-3), 279–302.
Turetken, E., Benmansour, F., Fua, P. (2012). Automated reconstruction of tree structures using path classifiers and mixed integer programming. In: Proceedings Computer Vision and Pattern Recognition (CVPR), IEEE Conference on. IEEE, Rhode Island, (pp. 566–573).
Turetken, E., Benmansour, F., Andres, B., H P, Fua, P. (2013). Reconstructing loopy curvilinear structures using integer programming. In: Proceedings CVPR. IEEE, Portland, (pp. 1822–1829).
Wang, Y., Narayanaswamy, A., Tsai, C.L., Roysam, B. (2011). A broadly applicable 3-D neuron tracing method based on open-curve snake. Neuroinformatics, 9(2-3), 193–217.
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. doi:10.1016/j.neuroscience.2005.05.053.
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.
Xie, J., Zhao, T., Lee, T., Myers, E., Peng, H. (2010). Automatic neuron tracing in volumetric microscopy images with anisotropic path searching. In: Proceedings Medical Image Computing and Computer-Assisted Intervention (pp. 472–479). Beijing: Springer
Xie, J., Zhao, T., Lee, T., Myersm, E., Peng, H. (2011). Anisotropic path searching for automatic neuron reconstruction. Medical image analysis, 15(5), 680–689.
Xiong, G., Zhou, X., Degterev, A., Ji, L., Wong, S. (2006). Automated neurite labeling and analysis in fluorecence microscopy images. Journal of the International Society for Analytical Cytology, 69A, 494–505. doi:10.1002/cyto.a.20296/pdf, http://onlinelibrary.wiley.com.ezproxy.lib.uh.edu.
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(4), 213–232.
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.
Zhou, Y., & Toga, A. (1999). Efficient skeletonization of volumetric objects. IEEE Trans Visual and Computer Graphics, 5(3), 196–209.
Acknowledgments
This work was supported in part by NSF-DMS 1320910, 0915242, 1008900, 1005799 and by NHARP-003652-0136-2009. I.A. Kakadiaris was also supported by the Hugh Roy and Lillie Cranz Cullen Endowment Fund. We thank our student P. Hernandez-Herrera for allowing us to use the performance test results he obtained with Neuronstudio, ORION and APP2 on numerous data sets. Last, but most certainly not least, we would like to thank the three reviewers for their encouraging comments and their valuable input which helped us improve the quality of the paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
Information Sharing Statement
Matlab source code of all binary segmentation, seeding and tracing routines are publicly available for download at http://www.math.uh.edu/~mpapadak/centerline
A manual is included in the same folder together with the source code. The code works for 3-D data sets, but it can be modified for use with 2-D sets. Please cite the above web address if you use our code for any publication or commercial purpose. All data sets used in our experiments are publicly available at the DIADEM competition (RRID:nif-0000-23194) website.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Jiménez, D., Labate, D., Kakadiaris, I.A. et al. Improved Automatic Centerline Tracing for Dendritic and Axonal Structures. Neuroinform 13, 227–244 (2015). https://doi.org/10.1007/s12021-014-9256-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12021-014-9256-z