Resolving clustered worms via probabilistic shape models
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Golland_Resolving clustered.pdf
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Author(s)
Wahlby, Carolina
Riklin-Raviv, Tammy
Ljosa, Vebjorn
Conery, Annie L.
Golland, Polina
Ausubel, Frederick M.
Carpenter, Anne E.
Date Issued
April 2010
Journal
IEEE International Symposium on Biomedical Imaging 2010
Publisher
Institute of Electrical and Electronics Engineers
Citation
Wahlby, Carolina et al. “Resolving Clustered Worms via Probabilistic Shape Models.” Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium On. 2010. 552-555. Copyright © 2010, IEEE
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ISBI.2010.5490286
