Automatic recognition of cells (ARC) for 3D images of C. elegans

  • Authors:

  • Fuhui Long;Hanchuan Peng;Xiao Liu;Stuart Kim;Gene Myers

  • Affiliations:

  • Howard Hughes Medical Institute, Ashburn, Virginia;Howard Hughes Medical Institute, Ashburn, Virginia;Department of Developmental Biology, Stanford University, Stanford, California;Department of Developmental Biology, Stanford University, Stanford, California;Howard Hughes Medical Institute, Ashburn, Virginia

  • Venue:
  • RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
  • Year:
  • 2008

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Abstract

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%.