Automated 3-D Detection of Dendritic Spines from In Vivo Two-Photon Image Stacks

  • P. K. Singh
  • P. Hernandez-Herrera
  • D. Labate
  • M. Papadakis
Original Article

Abstract

Despite the significant advances in the development of automated image analysis algorithms for the detection and extraction of neuronal structures, current software tools still have numerous limitations when it comes to the detection and analysis of dendritic spines. The problem is especially challenging in in vivo imaging, where the difficulty of extracting morphometric properties of spines is compounded by lower image resolution and contrast levels native to two-photon laser microscopy. To address this challenge, we introduce a new computational framework for the automated detection and quantitative analysis of dendritic spines in vivo multi-photon imaging. This framework includes: (i) a novel preprocessing algorithm enhancing spines in a way that they are included in the binarized volume produced during the segmentation of foreground from background; (ii) the mathematical foundation of this algorithm, and (iii) an algorithm for the detection of spine locations in reference to centerline trace and separating them from the branches to whom spines are attached to. This framework enables the computation of a wide range of geometric features such as spine length, spatial distribution and spine volume in a high-throughput fashion. We illustrate our approach for the automated extraction of dendritic spine features in time-series multi-photon images of layer 5 cortical excitatory neurons from the mouse visual cortex.

Keywords

Image processing Automated neural image segmentation Automated dendritic spine detection Two-photon microscopy 

References

  1. Agam, G., & Wu, C. (2005). Probabilistic modeling based vessel enhancement in thoracic ct scans, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005, CVPR 2005. IEEE, (Vol. 2 pp. 649–654).Google Scholar
  2. Bai, W., Zhou, X., Ji, L., Cheng, J., & Wong, S.T. (2007). Automatic dendritic spine analysis in two-photon laser scanning microscopy images. Cytometry Part A, 71(10), 818–826.CrossRefGoogle Scholar
  3. Bas, E., & Erdogmus, D. (2011). Principal curves as skeletons of tubular objects. Neuroinformatics, 9(2-3), 181–191.CrossRefPubMedGoogle Scholar
  4. Blumer, C., Vivien, C., Genoud, C., Perez-Alvarez, A., Wiegert, J.S., Vetter, T., & Oertner, T.G. (2015). Automated analysis of spine dynamics on live ca1 pyramidal cells. Medical Image Analysis, 19(1), 87–97.CrossRefPubMedGoogle Scholar
  5. Cheng, J., Zhou, X., Miller, E., Witt, R.M., Zhu, J., Sabatini, B.L., & Wong, S.T. (2007). A novel computational approach for automatic dendrite spines detection in two-photon laser scan microscopy. Journal of Neuroscience Methods, 165(1), 122–134.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Donohue, D.E., & Ascoli, G.A. (2011). Automated reconstruction of neuronal morphology: An overview. Brain Research Reviews, 67(1–2), 94–102. doi:10.1016/j.brainresrev.2010.11.003. http://www.sciencedirect.com/science/article/pii/S0165017310001293.CrossRefPubMedGoogle Scholar
  7. Fan, J., Zhou, X., Dy, J.G., Zhang, Y., & Wong, S.T. (2009). An automated pipeline for dendrite spine detection and tracking of 3d optical microscopy neuron images of in vivo mouse models. Neuroinformatics, 7 (2), 113–130.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Glaser, J.R., & Glaser, E.M. (1990). Neuron imaging with neurolucida — a pc-based system for image combining microscopy. Computerized Medical Imaging and Graphics, 14(5), 307–317. doi:10.1016/0895-6111(90)90105-K. http://www.sciencedirect.com/science/article/pii/089561119090105K, progress in Imaging in the Neurosciences Using Microcomputers and Workstations.CrossRefPubMedGoogle Scholar
  9. He, T., Xue, Z., & Wong, S.T. (2012a). A novel approach for three dimensional dendrite spine segmentation and classification, SPIE Medical Imaging, International Society for Optics and Photonics (pp. 831,437–831,437).Google Scholar
  10. He, T., Xue, Z., Kim, Y., & Wong, S.T. (2012b). Three-dimensional dendritic spine detection based on minimal cross-sectional curvature, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI). IEEE (pp. 1639–1642).Google Scholar
  11. Hernandez-Herrera, P., Papadakis, M., & Kakadiaris, I.A. (2014). Segmentation of neurons based on one-class classification, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). doi:10.1109/ISBI.2014.6868119 (pp. 1316–1319).CrossRefGoogle Scholar
  12. Hernandez-Herrera, P., Papadakis, M., & Kakadiaris, I.A. (2016). Multi-scale segmentation of neurons based on one-class classification. Journal of Neuroscience Methods, 266, 94–106. doi:10.1016/j.jneumeth.2016.03.019.CrossRefPubMedGoogle Scholar
  13. Herrera-Hernandez, P. (2015). 3-d morphology of neurons. Phd: University of Houston.Google Scholar
  14. Janoos, F., Mosaliganti, K., Xu, X., Machiraju, R., Huang, K., & Wong, S.T. (2009). Robust 3d reconstruction and identification of dendritic spines from optical microscopy imaging. Medical Image Analysis, 13(1), 167–179.CrossRefPubMedGoogle Scholar
  15. Jiménez, D., Papadakis, M., Labate, D., & Kakadiaris, I.A. (2013). Improved automatic centerline tracing for dendritic structures, 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI). IEEE (pp. 1050–1053).CrossRefGoogle Scholar
  16. Jimėnez, D, Labate, D., Kakadiaris, I.A., & Papadakis, M. (2015). Improved automatic centerline tracing for dendritic and axonal structures. Neuroinformatics, 13(2), 227–244. 10.1007/s12021-014-9256-z.Google Scholar
  17. Koh, Y.Y. (2001). Automated recognition algorithms for neural studies. Citeseer: PhD thesis.Google Scholar
  18. 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.CrossRefGoogle Scholar
  19. Li, Q., & Deng, Z. (2012). A surface-based 3-d dendritic spine detection approach from confocal microscopy images. IEEE Transactions on Image Processing, 21(3), 1223–1230. doi:10.1109/TIP.2011.2166973.CrossRefPubMedGoogle Scholar
  20. MBF Bioscience (2011). Autospine. http://www.mbfbioscience.com/autospine.
  21. Meijering, E. (2010). Neuron tracing in perspective. Cytometry Part A, 77(7), 693–704.CrossRefGoogle Scholar
  22. Morrison, P., & Zou, J.J. (2006). Skeletonization based on error reduction. Pattern Recognition, 39(6), 1099–1109.CrossRefGoogle Scholar
  23. Penzes, P., Cahill, M.E., Jones, K.A., VanLeeuwen, J.E., & Woolfrey, K.M. (2011). Dendritic spine pathology in neuropsychiatric disorders. Neural Neuroscience, 14, 285–293.Google Scholar
  24. Rodriguez, A., Ehlenberger, D.B., Dickstein, D.L., Hof, P.R., & Wearne, S.L. (2008). Automated three-dimensional detection and shape classification of dendritic spines from fluorescence microscopy images. PloS One, 3(4), e1997.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Sajo, M., Ellis-Davies, G., & Morishita, H. (2016). Lynx1 limits dendritic spine turnover in the adult visual cortex. Journal of Neuroscience, 36(36), 9472–9478.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Santamaria-Pang, A., Hernandez-Herrera, P., Papadakis, M., Saggau, P., & Kakadiaris, I.A. (2015). Automatic morphological reconstruction of neurons from multiphoton and confocal microscopy images using 3d tubular models. Neuroinformatics, 13(3), 297. doi:10.1007/s12021-014-9253-2.CrossRefPubMedGoogle Scholar
  27. Schaap, M., Manniesing, R., Smal, I., Van Walsum, T., Van Der Lugt, A., & Niessen, W. (2007). Bayesian tracking of tubular structures and its application to carotid arteries in cta. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2007 (pp. 562–570). Springer.Google Scholar
  28. Scorcioni, R, & Polavaram, S.A.G. (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.CrossRefPubMedPubMedCentralGoogle Scholar
  29. Shi, P., Huang, Y., & Hong, J. (2014). Automated three-dimensional reconstruction and morphological analysis of dendritic spines based on semi-supervised learning. Biomedical Optics Express, 5(5), 1541–1553.CrossRefPubMedPubMedCentralGoogle Scholar
  30. Swanger, S.A., Yao, X., Gross, C., & Bassell, G.J. (2011). Automated 4d analysis of dendritic spine morphology: applications to stimulus-induced spine remodeling and pharmacological rescue in a disease model. Molecular Brain, 4(1), 1.CrossRefGoogle Scholar
  31. Tyrrell, J.A., di Tomaso, E., Fuja, D., Tong, R., Kozak, K., Jain, R.K., & Roysam, B. (2007). Robust 3-d modeling of vasculature imagery using superellipsoids. IEEE Transactions on Medical Imaging, 26(2), 223–237.Google Scholar
  32. Zhang, Y., Chen, K., Baron, M., Teylan, M.A., Kim, Y., Song, Z., Greengard, P., & Wong, S.T. (2010). A neurocomputational method for fully automated 3d dendritic spine detection and segmentation of medium-sized spiny neurons. Neuroimage, 50(4), 1472–1484.CrossRefPubMedPubMedCentralGoogle Scholar
  33. Zhou, Y., & Toga, A.W. (1999). Efficient skeletonization of volumetric objects. IEEE Transactions on Visualization and Computer Graphics, 5(3), 196–209.CrossRefPubMedPubMedCentralGoogle Scholar
  34. Zhou, Y., Kaufman, A., & Toga, A.W. (1998). Three-dimensional skeleton and centerline generation based on an approximate minimum distance field. The Visual Computer, 14(7), 303–314.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Bioinformatics and Computational BiologyThe University of Texas MD Anderson Cancer CenterHoustonUSA
  2. 2.Department of MathematicsUniversity of HoustonHoustonUSA
  3. 3.Laboratorio de Imagenes y Vision por Computadora, Instituto de BiotecnologiaUniversidad Nacional Autonoma de Mexico CuernavacaMorelosMexico

Personalised recommendations