Morphology-guided graph search for untangling objects: C. elegans analysis

Authors:
Tammy Riklin Raviv;V. Ljosa;A. L. Conery;F. M. Ausubelc;A. E. Carpenter;P. Golland;C. Wählby
Affiliations:
Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA;Broad Institute of MIT and Harvard, Cambridge, MA;Dept. of Molecular Biology and Center for Computational and Integrative Biology, Mass. General Hospital, Boston, MA;Dept. of Molecular Biology and Center for Computational and Integrative Biology, Mass. General Hospital, Boston, MA;Broad Institute of MIT and Harvard, Cambridge, MA;Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA;Broad Institute of MIT and Harvard, Cambridge, MA
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Year:
2010

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Abstract

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.