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A Broadly Applicable 3-D Neuron Tracing Method Based on Open-Curve Snake

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Abstract

This paper presents a broadly applicable algorithm and a comprehensive open-source software implementation for automated tracing of neuronal structures in 3-D microscopy images. The core 3-D neuron tracing algorithm is based on three-dimensional (3-D) open-curve active Contour (Snake). It is initiated from a set of automatically detected seed points. Its evolution is driven by a combination of deforming forces based on the Gradient Vector Flow (GVF), stretching forces based on estimation of the fiber orientations, and a set of control rules. In this tracing model, bifurcation points are detected implicitly as points where multiple snakes collide. A boundariness measure is employed to allow local radius estimation. A suite of pre-processing algorithms enable the system to accommodate diverse neuronal image datasets by reducing them to a common image format. The above algorithms form the basis for a comprehensive, scalable, and efficient software system developed for confocal or brightfield images. It provides multiple automated tracing modes. The user can optionally interact with the tracing system using multiple view visualization, and exercise full control to ensure a high quality reconstruction. We illustrate the utility of this tracing system by presenting results from a synthetic dataset, a brightfield dataset and two confocal datasets from the DIADEM challenge.

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References

  • 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), 1338–50.

    Article  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 

  • Aylward, S. R., & Bullitt, E. (2002). Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction. IEEE Trans Medical Imaging, 21(2), 61–75.

    Article  Google Scholar 

  • Bauer, C., Bischof, H. (2008). A Novel Approach for Detection of Tubular Objects and Its Application to Medical Image Analysis. Proceedings of the 30th DAGM symposium on Pattern Recognition, 163–172.

  • Benmansour, F., Cohen, L. D. (2010). Tubular Structure segmentation based on minimal path method and anisotropic enhancement. International Journal of Computer Vision, 1–19.

  • Boykov, Y., Veksler, O., & Zabih, R. (2001). Efficient approximate energy minimization via graph cuts. IEEE Transactions on PAMI, 20(12), 1222–1239.

    Article  Google Scholar 

  • Cai, H., Xu, X., Lu, J., Lichtman, J., 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 

  • Cai, H., Xu, X., Lu, J., Lichtman, J., Yung, S. P., & Wong, S. T. (2008). Using nonlinear diffusion and mean shift to detect and connect cross-sections of axons in 3-D optical microscopy images. Medical Image Analysis, 12(6), 666–75.

    Article  PubMed  Google Scholar 

  • Cohen, LD., Kimmel R. (1997). Global minimum for active contour models: a minimal path approach. International Journal of Computer Vision, 24, 57–78.

    Google Scholar 

  • 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(2), 103–114.

    CAS  Google Scholar 

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

    Article  Google Scholar 

  • Fiala, J. C. (2005). Reconstruct: a free editor for serial section microscopy. Journal of Microscopy, 218, 52–61.

    Article  PubMed  CAS  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 

  • Freiman, M., Broide, N., Natanzon, M., Nammer, E., Shilon, O., Weizman, L., Joshowicz, L., & Sosna, J. (2009). Vessels-cut: a graph based approach to patient-specific carotid arteries modeling. Proc. 2nd workshop on: 3D Physiological Human, Springer LNCS, 5903, 1–12.

  • Freund, Y., & Schapire, R. E. (1999). A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14, 771–780.

    Google Scholar 

  • Fridman, Y., Pizer, S. M., Aylward, S., & Bullitt, E. (2003). Segmenting 3-D branching tubular structures using cores. MICCAI, 2003, 570–577.

    Google Scholar 

  • González, G., Türetken, E., Fleuret, F., Fua, P. (2010). Delineating trees in 2-D images and 3-D image-stacks. Proc. of the IEEE international conference on Computer Vision and Pattern Recognition (CVPR), 2799–2806.

  • Hamarneh, G., & Jassi, P. (2010). VascuSynth: simulating vascular trees for generating volumetric image data with ground-truth segmentation and tree analysis. Computerized Medical Imaging and Graphics, 34(8), 605–16.

    Article  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 brigheld images. Proc. of Microscopy and Microanalysis, 9(4), 296–310.

    Google Scholar 

  • Kong, K. Y., Marcus, A., Young, H. J., Giannakakou, P., & Wang, M. (2005). Computer assisted analysis of microtubule dynamics in living cells. Conference of the IEEE Engineering in Medicine and Biology Society, 4, 3982–5.

    Google Scholar 

  • Krissian, K., Malandain, G., Ayache, N., Vaillant, R., & Trousset, Y. (2000). Model based detection of tubular structures in 3-D images. Computer Vision and Image Understanding, 80(2), 130–171.

    Article  Google Scholar 

  • Lesage, D., Angelini, E. D., Bloch, I., Funka-Lea, G.. (2009). Design and study of flux-based features for 3-D vascular tracking. IEEE International Symposium on Biomedical Imaging 286–289.

  • Li, H., Shen, T., Smith, M. B., Fujiwara, I., Vavylonis, D., Huang, X. (2009). Automated actin filament segmentation, tracking, and tip elongation measurements based on open active contour models. Proc. of the IEEE Int’l Symposium on Biomedical Imaging: From Nano to Macro (ISBI), 1302–1305.

  • Lu, J., Fiala, J. C., & Lichtman, J. W. (2009). Semi-automated reconstruction of neural processes from large numbers of fluorescence images. PLoS ONE, 4(5), e5655.

    Article  PubMed  Google Scholar 

  • Luisi, J., Narayanaswamy, A., Galbreath, Z., Roysam, B. (2011). The farsight trace editor. Neuroinformatics. Submitted.

  • Mann, A. (2010). Teams battle for neuron prize. Nature, 467, 143.

    Article  PubMed  CAS  Google Scholar 

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

    Article  Google Scholar 

  • Meijering, E., Jacob, M., Sarria, J.-C. F., Unser, M. (2003). A novel approach to neurite tracing in fluorescence microscopy images. International Conference on Signal and Image Processing, 491–495.

  • Mohan, V., Sundaramoorthi, G., Stillman, A., & Tannenbaum, A. (2009). Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. Int Conf MICCAI: Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling.

    Google Scholar 

  • Narayanaswamy, A., Wang, Y., & Roysam, B. (2011). A Preprocessing Pipeline to Enhance 3-D Images of Neuronal Arbors. Submitted: Neuroinformatics.

    Google Scholar 

  • Narayanaswamy, A., Dwarakapuram, S., Bjornsson, C. S., Cutler, B. M., Shain, W., & Roysam, B. (2010). Robust adaptive 3-D segmentation of vessel laminae from fluorescence confocal microscope images and parallel GPU implementation. IEEE Transactions on Medical Imaging, 29(3), 583–97.

    Article  PubMed  Google Scholar 

  • Peng, H. C., Ruan, Z. C., Atasoy, D., & Sternson, S. (2010a). Automatic reconstruction of 3-D neuron structures using a graph-augmented deformable model. Bioinformatics, 26(12), i38–46.

    Article  CAS  Google Scholar 

  • Peng, H. C., Ruan, Z. C., Long, F. H., Simpson, J. H., & Myers, E. W. (2010b). V3D enables real-time 3-D visualization and quantitative analysis of large-scale biological image data sets. Nature Biotechnology, 28, 348–353.

    Article  CAS  Google Scholar 

  • Pock, T., Beichel, R., Bischof, H. (2005). A novel robust tube detection filter for 3-D centerline extraction. 18th Symposium on Operating System Principles, 481–490.

  • Pool, M., Thiemann, J., Bar-Or, A., & Fournier, A. E. (2008). NeuriteTracer: a novel ImageJ plugin for automated quantification of neurite outgrowth. Journal of Neuroscience Methods, 168, 134–139.

    Article  PubMed  Google Scholar 

  • 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, 1283–1298.

    Article  PubMed  Google Scholar 

  • Sethian, J. A. (1996). Level set methods and fast marching methods. Cambridge University Press.

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

    Article  Google Scholar 

  • Srinivasan, R., Zhou, X. B., Miller, E., Lu, J., Litchman, J., & Wong, S. T. C. (2010). Reconstruction of the neuromuscular junction connectome. Bioinformatics, 26(12), i64–i70.

    Article  PubMed  CAS  Google Scholar 

  • Tyrrell, J A. (2006). Modeling and analysis of tubular structures in medical images: With applications to fluorescence microscopy. Rensselaer Polytechnic Institute, Phd Thesis.

  • 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  Google Scholar 

  • Wang, J., Zhou, X. B., Lu, J., Lichtman, J., Chang, S. F., Wong, S. T. C. (2007). Dynamic local tracing for 3-D axon curvilinear structure detection from microscopic image stack. In IEEE International Symposium of Biomedical Imaging (ISBI), 81–84.

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

    Article  PubMed  CAS  Google Scholar 

  • Xie, J., Zhao, T., Lee, T., Myers, E., & Peng, H. (2010). Automatic neuron tracing in volumetric microscopy images with anisotropic path searching. MICCAI, 13(2), 472–9.

    PubMed  Google Scholar 

  • Xu, C., & Prince, J. L. (1998). Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing, 7(3), 359–369.

    Article  PubMed  CAS  Google Scholar 

  • Yuan, X., Trachtenberg, J. T., Potter, S. M., & 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  Google Scholar 

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Acknowledgements

This work was supported by NIH Grant R01 EB005157.

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Authors and Affiliations

  1. ECSE Department, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    Yu Wang, Arunachalam Narayanaswamy & Badrinath Roysam

  2. Computer Science Department, Iona College, New Rochelle, NY, USA

    Chia-Ling Tsai

  3. Electrical & Computer Engineering, University of Houston, N325, Engineering Building 1, Houston, TX, 77204, USA

    Badrinath Roysam

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  1. Yu Wang
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  2. Arunachalam Narayanaswamy
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  4. Badrinath Roysam
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Correspondence to Badrinath Roysam.

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Wang, Y., Narayanaswamy, A., Tsai, CL. et al. A Broadly Applicable 3-D Neuron Tracing Method Based on Open-Curve Snake. Neuroinform 9, 193–217 (2011). https://doi.org/10.1007/s12021-011-9110-5

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