Automatic Recognition of Cells (ARC) for 3D Images of C. elegans

  • Fuhui Long
  • Hanchuan Peng
  • Xiao Liu
  • Stuart Kim
  • Gene Myers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4955)


The development of high-resolution microscopy makes possible the high-throughput screening of cellular information, such as gene expression at single cell resolution. One of the critical enabling techniques yet to be developed is the automatic recognition or annotation of specific cells in a 3D image stack. In this paper, we present a novel graph-based algorithm, ARC, that determines cell identities in a 3D confocal image of C. elegans based on their highly stereotyped arrangement. This is an essential step in our work on gene expression analysis of C. elegans at the resolution of single cells. Our ARC method integrates both the absolute and relative spatial locations of cells in a C. elegans body. It uses a marker-guided, spatially-constrained, two-stage bipartite matching to find the optimal match between cells in a subject image and cells in 15 template images that have been manually annotated and vetted. We applied ARC to the recognition of cells in 3D confocal images of the first larval stage (L1) of C. elegans hermaphrodites, and achieved an average accuracy of 94.91%.


Bipartite Graph Graph Match Adjacency Matrice Template Image Average Recognition Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Fuhui Long
    • 1
  • Hanchuan Peng
    • 1
  • Xiao Liu
    • 2
  • Stuart Kim
    • 2
  • Gene Myers
    • 1
  1. 1.Janelia Farm Research CampusHoward Hughes Medical InstituteAshburnUSA
  2. 2.Department of Developmental BiologyStanford UniversityStanfordUSA

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