OSA's Digital Library

Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 5, Iss. 5 — May. 1, 2014
  • pp: 1541–1553

Automated three-dimensional reconstruction and morphological analysis of dendritic spines based on semi-supervised learning

Peng Shi, Yue Huang, and Jinsheng Hong  »View Author Affiliations


Biomedical Optics Express, Vol. 5, Issue 5, pp. 1541-1553 (2014)
http://dx.doi.org/10.1364/BOE.5.001541


View Full Text Article

Enhanced HTML    Acrobat PDF (1268 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

A dendritic spine is a small membranous protrusion from a neuron's dendrite that typically receives input from a single synapse of an axon. Recent research shows that the morphological changes of dendritic spines have a close relationship with some specific diseases. The distribution of different dendritic spine phenotypes is a key indicator of such changes. Therefore, it is necessary to classify detected spines with different phenotypes online. Since the dendritic spines have complex three dimensional (3D) structures, current neuron morphological analysis approaches cannot classify the dendritic spines accurately with limited features. In this paper, we propose a novel semi-supervised learning approach in order to perform the online morphological classification of dendritic spines. Spines are detected by a new approach based on wavelet transform in the 3D space. A small training data set is chosen from the detected spines, which has the spines labeled by the neurobiologists. The remaining spines are then classified online by the semi-supervised learning (SSL) approach. Experimental results show that our method can quickly and accurately analyze neuron images with modest human intervention.

© 2014 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.5010) Image processing : Pattern recognition
(100.6890) Image processing : Three-dimensional image processing

ToC Category:
Image Processing

History
Original Manuscript: February 18, 2014
Revised Manuscript: April 10, 2014
Manuscript Accepted: April 10, 2014
Published: April 17, 2014

Citation
Peng Shi, Yue Huang, and Jinsheng Hong, "Automated three-dimensional reconstruction and morphological analysis of dendritic spines based on semi-supervised learning," Biomed. Opt. Express 5, 1541-1553 (2014)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-5-5-1541


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. S. Knafo, L. Alonso-Nanclares, J. Gonzalez-Soriano, P. Merino-Serrais, I. Fernaud-Espinosa, I. Ferrer, and J. DeFelipe, “Widespread changes in dendritic spines in a model of Alzheimer’s disease,” Cereb. Cortex19(3), 586–592 (2009). [CrossRef] [PubMed]
  2. T. L. Spires, M. Meyer-Luehmann, E. A. Stern, P. J. McLean, J. Skoch, P. T. Nguyen, B. J. Bacskai, and B. T. Hyman, “Dendritic spine abnormalities in amyloid precursor protein transgenic mice demonstrated by gene transfer and intravital multiphoton microscopy,” J. Neurosci.25(31), 7278–7287 (2005). [CrossRef] [PubMed]
  3. D. M. Hartley, C. P. Ye, T. Diehl, S. Vasquez, P.M. Vassilev, D. B. Teplow, and D. J. Selkoe, “Protofibrillar Intermediates of amyloid beta-protein induce acute electrophysiological changes and progressive neurotoxicity in cortical neurons,” J. Neurosci. Methods19(20), 9 (1999).
  4. J. B. Pawley, Handbook of Biological Confocal Microscopy. 3rd ed., Springer, 988 (2006).
  5. E. A. Nimchinsky, B. L. Sabatini, and K. Svoboda, “Structure and function of dendritic spines,” Annu. Rev. Physiol.64(1), 313–353 (2002). [CrossRef] [PubMed]
  6. Y. Zhang, X. Zhou, R. M. Witt, B. L. Sabatini, D. Adjeroh, and S. T. Wong, “Dendritic spine detection using curvilinear structure detector and LDA classifier,” Neuroimage36(2), 346–360 (2007). [CrossRef] [PubMed]
  7. J. Cheng, E. Miller, R. M. Witt, J. Zhu, B. L. Sabatini, and S. T. C. Wong, “A novel computational approach for automated dendrite spines detection in two-photon laser scan microscopy,” J. Neurosci. Methods165(1), 13 (2007). [CrossRef] [PubMed]
  8. W. Bai, L. Ji, J. Cheng, and S.T.C. Wong, “Automated dendritic spine analysis in two-photon laser scanning microscopy images,” Cytometry A71(10), 9 (2007). [PubMed]
  9. F. Janoos, X. Xu, R. Machiraju, K. Huang, and S. T. C. Wong, “Robust 3D reconstruction and identification of dendritic spines from optical microscopy imaging,” Med. Image Anal.13(1), 167–179 (2009). [CrossRef] [PubMed]
  10. A. Rodriguez, D. L. Dickstein, P. R. Hof, and S. L. Wearne, “Automated three-dimensional detection and shape classification of dendritic spines from fluorescence microscopy images,” PLOS one, 3(4), e1997 (2008).
  11. A. Rodriguez, D. B. Ehlenberger, P. R. Hof, and S. L. Wearne, “Rayburst sampling, an algorithm for automated three-dimensional shape analysis from laser scanning microscopy images,” Nat. Protoc.1(4), 2152–2161 (2006). [CrossRef] [PubMed]
  12. M. Matsuzaki, G. C. Ellis-Davies, and H. Kasai, “Three-dimensional mapping of unitary synaptic connections by two-photon macro photolysis of caged glutamate,” J. Neurophysiol.99(3), 1535–1544 (2008). [CrossRef] [PubMed]
  13. R. Yuste and W. Denk, “Dendritic spines as basic functional units of neuronal integration,” Nature375(6533), 682–684 (1995). [CrossRef] [PubMed]
  14. K. Svoboda and R. Yasuda, “Principles of two-photon excitation microscopy and its applications to neuroscience,” Neuron50(6), 823–839 (2006). [CrossRef] [PubMed]
  15. Q. Li, X. B. Zhou, Z. Deng, M. Baron, M. A. Teylan, Y. Kim, and S. T. C. Wong, A Novel Surface-based Geometric Approach for 3D Dendritic Spine Detection from Multi-phonton Excitation Microscopy Images, in The Sixth IEEE International Symposium on Biomedical Imaging. IEEE: Boston, MA, U.S. (2009).
  16. H. Hering and M. Sheng, “Dendritic spines: structure, dynamics and regulation,” Nat. Rev. Neurosci.2(12), 880–888 (2001). [CrossRef] [PubMed]
  17. Y. Hayashi and A. K. Majewska, “Dendritic spine geometry: functional implication and regulation,” Neuron46(4), 529–532 (2005). [CrossRef] [PubMed]
  18. O. Chapelle and A. Zien, Semi-Supervised Learning (Cambridge: MIT Press 2006).
  19. D. Zhou, T. N. Lal, J. Weston, and B. Scholkopf, Learning with Local and Global Consistency. Advances in Neural Information Processing Systems, 16, 321–8 (2004).
  20. P. E. Greenwood, A Guide to Chi-Squared Testing, 280 (John Wiley & Sons, 1996).
  21. A. Tashiro and R. Yuste, “Structure and molecular organization of dendritic spines,” Histol. Histopathol.18(2), 617–634 (2003). [PubMed]
  22. J. Shawe-Taylor and N. Crisianini, Support Vector Machines and Other Kernel-Based Learning Methods (Cambridge, UK: Cambridge University Press 2000).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited