Morphology-Guided Graph Search for Untangling Objects: C. elegans Analysis

  • T. Riklin Raviv
  • V. Ljosa
  • A. L. Conery
  • F. M. Ausubel
  • A. E. Carpenter
  • P. Golland
  • C. Wählby
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6363)


We present a novel approach for extracting cluttered objects based on their morphological properties. Specifically, we address the problem of untangling Caenorhabditis elegans clusters in high-throughput screening experiments. We represent the skeleton of each worm cluster by a sparse directed graph whose vertices and edges correspond to worm segments and their adjacencies, respectively. We then search for paths in the graph that are most likely to represent worms while minimizing overlap. The worm likelihood measure is defined on a low-dimensional feature space that captures different worm poses, obtained from a training set of isolated worms. We test the algorithm on 236 microscopy images, each containing 15 C. elegans worms, and demonstrate successful cluster untangling and high worm detection accuracy.


Medial Axis Graph Vertex Graph Search Path Candidate Worm Count 
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 2010

Authors and Affiliations

  • T. Riklin Raviv
    • 1
  • V. Ljosa
    • 2
  • A. L. Conery
    • 3
  • F. M. Ausubel
    • 3
  • A. E. Carpenter
    • 2
  • P. Golland
    • 1
  • C. Wählby
    • 2
  1. 1.Computer Science and Artificial Intelligence LaboratoryMITCambridge
  2. 2.Imaging PlatformBroad Institute of MIT and HarvardCambridge
  3. 3.Dept. of Molecular Biology and Center for Computational and Integrative BiologyMass. General HospitalBoston

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