The Visual Computer

, Volume 27, Issue 12, pp 1055–1069 | Cite as

Automated cell classification and visualization for analyzing remyelination therapy

  • Koel Das
  • Aditi Majumder
  • Monica Siegenthaler
  • Hans Keirstead
  • M. Gopi
Original Article

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.

Keywords

Cell detection Geometric processing Progressive isocontour 

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Koel Das
    • 1
  • Aditi Majumder
    • 2
  • Monica Siegenthaler
    • 3
  • Hans Keirstead
    • 4
  • M. Gopi
    • 2
  1. 1.South Asian UniversityDelhiIndia
  2. 2.Department of Computer ScienceUniversity of California, IrvineIrvineUSA
  3. 3.California Stem Cell, IncIrvineUSA
  4. 4.Department of Anatomy and NeurobiologyUniversity of California, IrvineIrvineUSA

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