Carolina Wählby, Tammy Riklin-Raviv, Vebjorn Ljosa, Annie L. Conery, Polina Golland, Frederick M. Ausubel, and Anne E. Carpenter:
“Resolving clustered worms via probabilistic shape models,”
in Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI), p. 552–555, April, 2010, doi:10.1109/ISBI.2010.5490286.
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 because the worms’ tendency to cluster has prevented 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 presented.