TY - JOUR
T1 - Multi-environment model estimation for motility analysis of Caenorhabditis elegans
AU - Sznitman, Raphael
AU - Gupta, Manaswi
AU - Hager, Gregory D.
AU - Arratia, Paulo E.
AU - Sznitman, Josué
N1 - Funding Information:
Strains of C. elegans were obtained from the Caenorhabditis elegans Genetic Stock Center (University of Minnesota), supported by the National Institutes of Health (Bethesda MD, USA). The authors would like to thank Dr. R. Ghosh (Lewis-Sigler Institute for Integrative Genomics, Princeton U.), X. Shen (Department of Mechanical Engineering and Applied Mechanics, U. of Penn), and Dr. A.E.X. Brown (MRC Lab of Molecular Biology, Cambridge U.) for their support and helpful discussions.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pone.0011631
DO - 10.1371/journal.pone.0011631
M3 - Article
C2 - 20661478
AN - SCOPUS:77955354466
SN - 1932-6203
VL - 5
JO - PloS one
JF - PloS one
IS - 7
M1 - e11631
ER -