Multi-Environment Model Estimation for Motility Analysis of Caernorhabditis elegans

Sznitman, Raphael; Gupta, Manaswi; Hager, Gregory; Arratia, Paulo; Sznitman, Josue (2010). Multi-Environment Model Estimation for Motility Analysis of Caernorhabditis elegans. PLoS ONE, 5(7), e11631. Public Library of Science 10.1371/journal.pone.0011631

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The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode ‘skeletons’ for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and ‘skeletonizing’ across a wide range of motility assays.

Item Type:

Journal Article (Original Article)


10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory

UniBE Contributor:

Sznitman, Raphael


000 Computer science, knowledge & systems
600 Technology > 620 Engineering




Public Library of Science




Raphael Sznitman

Date Deposited:

27 May 2015 07:42

Last Modified:

05 Dec 2022 14:47

Publisher DOI:





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