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Classifying Stem Cell Differentiation Images by Information Distance

  • Xianglilan Zhang
  • Hongnan Wang
  • Tony J. Collins
  • Zhigang Luo
  • Ming Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7523)

Abstract

The ability of stem cells holds great potential for drug discovery and cell replacement therapy. To realize this potential, effective high content screening for drug candidates is required. Analysis of images from high content screening typically requires DNA staining to identify cell nuclei to do cell segmentation before feature extraction and classification. However, DNA staining has negative effects on cell growth, and segmentation algorithms err when compound treatments cause nuclear or cell swelling/shrinkage. In this paper, we introduced a novel Information Distance Classification (IDC) method, requiring no segmentation or feature extraction; hence no DNA staining is needed. In classifying 480 candidate compounds that may be used to stimulate stem cell differentiation, the proposed IDC method was demonstrated to achieve a 3% higher F1 score than conventional analysis. As far as we know, this is the first work to apply information distance in high content screening.

Keywords

information distance stem cell differentiation image classification compound classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xianglilan Zhang
    • 1
    • 2
    • 3
  • Hongnan Wang
    • 1
    • 2
    • 3
  • Tony J. Collins
    • 1
    • 2
    • 3
  • Zhigang Luo
    • 1
    • 2
    • 3
  • Ming Li
    • 1
    • 2
    • 3
  1. 1.School of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.David R. Cheriton School of Computer ScienceUniversity of WaterlooWaterlooCanada
  3. 3.Stem Cell and Cancer Research InstituteMcmaster UniversityHamiltonCanada

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