Resolving clustered worms via probabilistic shape models

Authors:
Carolina Wählby;Tammy Riklin-Raviv;Vebjorn Ljosa;Annie L. Conery;Polina Golland;Frederick M. Ausubel;Anne E. Carpenter
Affiliations:
Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA;Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA;Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA;Dept. of Molecular Biology and Center for Computational and Integrative Biology, MGH, Boston, MA;Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA;-;Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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Abstract

The roundworm Caenorhabditis elegans is an effective model system for biological processes such as immunity, behavior, and metabolism. Robotic sample preparation together with automated microscopy and image analysis has recently enabled high-throughput screening experiments using C. elegans. So far, such experiments have been limited to per-image measurements due to the tendency of the worms to cluster, which prevents extracting features from individual animals. We present a novel approach for the extraction of individual C. elegans from clusters of worms in high-throughput microscopy images. The key ideas are the construction of a low-dimensional shape-descriptor space and the definition of a probability measure on it. Promising segmentation results are shown.