Skip to main content

Advertisement

SpringerLink
Log in

Automated cell classification and visualization for analyzing remyelination therapy

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Remyelination therapy is a state-of-the-art technique for treating spinal cord injury (SCI). Demyelination—the loss of myelin sheath that insulates axons, is a prominent feature in many neurological disorders resulting in SCI. This lost myelin sheath can be replaced by remyelination. In this paper, we propose an algorithm for efficient automated cell classification and visualization to analyze the progress of remyelination therapy in SCI. Our method takes as input the images of the cells and outputs a density map of the therapeutically important oligodendrocyte-remyelinated axons (OR-axons) which is used for efficacy analysis of the therapy. Our method starts with detecting cell boundaries using a robust, shape-independent algorithm based on iso-contour analysis of the image at progressively increasing intensity levels. The detected boundaries of spatially clustered cells are then separated using the Delaunay triangulation based contour separation method. Finally, the OR-axons are identified and a density map is generated for efficacy analysis of the therapy. Our efficient automated cell classification and visualization of remyelination analysis significantly reduces error due to human subjectivity. We validate the accuracy of our results by extensive cross-verification by the domain experts.

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

Similar content being viewed by others

References

  1. Aguado, A., Nixon, M., Montiel, M.: Parameterizing arbitrary shapes via Fourier descriptors for evidence-gathering extraction. Comput. Vis. Image Underst. 69, 202–221 (1998)

    Article  Google Scholar 

  2. Amenta, N., Bern, M., Eppstein, D.: The crust and the β-skeleton: Combinatorial curve reconstruction. Graph. Models Image Process. 60(2), 125–135 (1998)

    Article  Google Scholar 

  3. Angulo, J., Flandrin, G.: Automated detection of working area of peripheral blood smears using mathematical morphology. Anal. Cell. Pathol. 25(1), 37–49 (2003)

    Google Scholar 

  4. Blakemore, W., Keirstead, H.: The origin of remyelinating cells in the central nervous system. J. Neuroimmunol. 98, 69–76 (1999)

    Article  Google Scholar 

  5. Blight, A.: Cellular morphology of chronic spinal cord injury in the cat: analysis of myelinated axons by line-sampling. Neuroscience 10, 521–543 (1983)

    Article  Google Scholar 

  6. Cahn, R., Poulsen, R., Toussaint, G.: Segmentation of cervical cell images. J. Histochem. Cytochem. 25(7), 681–688 (1977)

    Article  Google Scholar 

  7. Caselles, V., Catte, F., Coll, T., Dibos, F.: A geometric model for active contours in image processing. Numer. Math. 66(4), 1–31 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  8. Chari, D., Blakemore, W.: New insights into remyelination failure in multiple sclerosis: implications for glial cell transplantation. Mult. Scler. 8, 271–277 (2002)

    Article  Google Scholar 

  9. Das, K., Majumder, A., Siegenthaler, M., Keirstead, H., Gopi, M.: Automated analysis of remyelination therapy for spinal cord injury. In: Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP ’10, pp. 314–321. ACM, New York (2010)

    Chapter  Google Scholar 

  10. Daul, C., Graebling, P., Hirsch, E.: From the hough transform to a new approach for the detection and approximation of elliptical arcs. Comput. Vis. Image Underst. 72, 215–236 (1998)

    Article  Google Scholar 

  11. Doraiswamy, H., Natarajan, V.: Efficient algorithms for computing Reeb graphs. Comput. Geom., Theory Appl. 42(6–7), 606–616 (2009)

    MathSciNet  MATH  Google Scholar 

  12. Garrido, A., de la Blanca, N.P.: Applying deformable templates for cell image segmentation. Pattern Recognit. 33(5), 821–832 (2000)

    Article  Google Scholar 

  13. Goto, T., Hoshino, Y.: Electrophysiological, histological, and behavioral studies in a cat with acute compression of the spinal cord. J. Orthop. Sci. 6, 59–67 (2001)

    Article  Google Scholar 

  14. Guy, J., Ellis, E.A., Kelley, K., Hope, G.M.: Spectra of G-ratio, myelin sheath thickness, and axon and fiber diameter in the guinea pig optic nerve. J. Comp. Neurol. 287, 446–454 (1989)

    Article  Google Scholar 

  15. Gyulassy, A., Bremer, P.-T., Hamann, B., Pascucci, V.: A practical approach to Morse-Smale complex computation: Scalability and generality. IEEE Trans. Vis. Comput. Graph. 14 (2008)

  16. Hagyard, D., Razaz, M., Atkin, P.: Analysis of watershed algorithms for greyscale images. In: ICIP, vol. III, pp. 41–44 (1996)

    Google Scholar 

  17. Herzberg, A.J., Kerns, B.J., Pollack, S.V., Kinney, R.B.: DNA image cytometry of keratoacanthoma and squamous cell carcinoma. J. Invest. Dermatol. 97, 495–500 (1991)

    Article  Google Scholar 

  18. Jones, T., Carpenter, A., Golland, P.: Voronoi-based segmentation of cells on image manifolds. In: ICPR, vol. 2, pp. 286–289 (2002)

    Google Scholar 

  19. Kass, M., Witkin, A., Terzopoulos, D.: Snakes—active contour models. Int. J. Comput. Vis. 1–4, 321–331 (1987)

    Google Scholar 

  20. Keirstead, H.: Stem cells for the treatment of myelin loss. Trends Neurosci. 28, 677–683 (2005)

    Article  Google Scholar 

  21. Li, Y., Field, P., Raisman, G.: Death of oligodendrocytes and microglial phagocytosis of myelin precede immigration of Schwann cells into the spinal cord. J. Neurocytol. 28, 417–427 (1999)

    Article  Google Scholar 

  22. Liu, L., Sclaroff, S.: Medical image segmentation and retrieval via deformable models. In: Proc. International Conference on Image Processing, Oct. 7–10, vol. 3, pp. 1071–1074 (2001)

    Google Scholar 

  23. Malpica, N., de Solórzano, C.O., Vaquero, J.J., Santos, A., Vallcorba, I., Garc’ıa-Sagredo, J.M., del Pozo, F.: Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry 28(4), 289–297 (1997)

    Article  Google Scholar 

  24. McTigue, D., Horner, P., Strokes, B., Gage, F.: Neurotrophin-3 and brain-derived neurotrophic factor induce oligodendrocyte proliferation and myelination of regenerating axons in the contused adult rat spinal cord. J. Neurosci. 18, 5354–5365 (1998)

    Google Scholar 

  25. Meyer, J., Velasco, K., Gopi, M.: Tracking of oligodendrocyte remyelinated axons in spinal cords. In: AIChE (2008). (Poster)

    Google Scholar 

  26. Milnor, J.: Morse Theory. Princeton University Press, Princeton (1969)

    Google Scholar 

  27. Najman, L., Schmitt, M.: Geodesic saliency of watershed contours and hierarchical segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18, 1163–1173 (1996)

    Article  Google Scholar 

  28. Park, J., Keller, J.: Snakes on the watershed. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1201–1205 (2001)

    Article  Google Scholar 

  29. Prineas, J.: Pathology of the early lesion in multiple sclerosis. Hum. Pathol. 6, 531–534 (1975)

    Article  Google Scholar 

  30. Watershed, B.S.: hierarchical segmentation and waterfall algorithm. In: Mathematical Morphology and its Applications to Image Processing, pp. 69–76 (1994)

    Google Scholar 

  31. Salgado-Ceballos, H., Guizar-Sahagun, G., Feria-Velasco, A., Grijalva, I., Espitia, L., Ibarra, A., Madrazo, I.: Spontaneous long-term remyelination after traumatic spinal cord injury in rats. Brain Res. 782, 126–135 (1998)

    Article  Google Scholar 

  32. Schnorrenberg, F., Pattichis, C., Kyriacou, K., Schizas, C.: Computer-aided detection of breast cancer nuclei. IEEE Trans. Inf. Technol. Biomed. 1(2), 128–140 (1997)

    Article  Google Scholar 

  33. Scolding, N., Franklin, R.: Remyelination in demyelinating disease. Baillière’s Clin. Neurol. 6, 525–548 (1997)

    Google Scholar 

  34. Strangel, M., Hartung, H.: Remyelinating strategies for the treatment of multiple sclerosis. Prog. Neurobiol. 68, 361–376 (2002)

    Article  Google Scholar 

  35. Thiran, J.-P., Macq, B.: Morphological feature extraction for the classification of digital images of cancerous tissues. IEEE Trans. Biomed. Eng. 43(10), 1011–1020 (1996)

    Article  Google Scholar 

  36. Totoiu, M., Keirstead, H.: Spinal cord injury is accompanied by chronic progressive demyelination. J. Comp. Neurol. 486, 373–383 (2005)

    Article  Google Scholar 

  37. Tsai, D.: An improved generalized hough transform for the recognition of overlapping objects. Image Vis. Comput. 15, 877–888 (1997)

    Article  Google Scholar 

  38. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  39. Waxman, S.: Demyelination in spinal cord injury. J. Neurol. Sci. 91, 1–14 (1989)

    Article  Google Scholar 

  40. Waxman, S.: Demyelination in spinal cord injury and multiple sclerosis: what can we do to enhance functional recovery? J. Neurotrauma 9, S105–117 (1992)

    Google Scholar 

  41. Wu, D., Zhang, Q.: A novel approach for cell segmentation based on directional information. In: ICBBE 2007, July 2007, pp. 920–923 (2007)

    Google Scholar 

  42. Wu, H.S., Barba, J., Gil, J.: Iterative thresholding for segmentation of cells from noisy images. J. Microsc. 197(3), 296–304 (2000)

    Article  Google Scholar 

  43. Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  44. Zhou, X., Cao, X., Perlman, Z., Wong, S.T.C.: A computerized cellular imaging system for high content analysis in monastrol suppressor screens. J. Biomed. Inform. 39(2), 115–125 (2006)

    Article  Google Scholar 

  45. Zimmer, C., Labruyere, E., Meas-Yedid, V., Guillen, N., Olivo-Marin, J.-C.: Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing. IEEE Trans. Med. Imaging 21(10), 1212–1221 (2002)

    Article  Google Scholar 

  46. Zimmer, C., Labruyere, E., Meas-Yedid, V., Guillen, N., Olivo-Marin, J.-C.: Improving active contours for segmentation and tracking of motile cells in videomicroscopy. In: Computer Vision for Biomedical Image Applications, vol. 3765 (2005). (Poster)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. South Asian University, Delhi, India

    Koel Das

  2. Department of Computer Science, University of California, Irvine, Irvine, CA, USA

    Aditi Majumder & M. Gopi

  3. California Stem Cell, Inc, Irvine, CA, USA

    Monica Siegenthaler

  4. Department of Anatomy and Neurobiology, University of California, Irvine, Irvine, CA, USA

    Hans Keirstead

Authors
  1. Koel Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aditi Majumder
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Monica Siegenthaler
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hans Keirstead
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. Gopi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Koel Das.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Das, K., Majumder, A., Siegenthaler, M. et al. Automated cell classification and visualization for analyzing remyelination therapy. Vis Comput 27, 1055–1069 (2011). https://doi.org/10.1007/s00371-011-0655-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-011-0655-y

Keywords

Search

Navigation