Cargando…
Multi-Environment Model Estimation for Motility Analysis of Caenorhabditis elegans
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...
Autores principales: | , , , , |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2010
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2908547/ https://www.ncbi.nlm.nih.gov/pubmed/20661478 http://dx.doi.org/10.1371/journal.pone.0011631 |
_version_ | 1782184191554748416 |
---|---|
author | Sznitman, Raphael Gupta, Manaswi Hager, Gregory D. Arratia, Paulo E. Sznitman, Josué |
author_facet | Sznitman, Raphael Gupta, Manaswi Hager, Gregory D. Arratia, Paulo E. Sznitman, Josué |
author_sort | Sznitman, Raphael |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-2908547 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-29085472010-07-26 Multi-Environment Model Estimation for Motility Analysis of Caenorhabditis elegans Sznitman, Raphael Gupta, Manaswi Hager, Gregory D. Arratia, Paulo E. Sznitman, Josué PLoS One Research Article 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. Public Library of Science 2010-07-22 /pmc/articles/PMC2908547/ /pubmed/20661478 http://dx.doi.org/10.1371/journal.pone.0011631 Text en Sznitman et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Sznitman, Raphael Gupta, Manaswi Hager, Gregory D. Arratia, Paulo E. Sznitman, Josué Multi-Environment Model Estimation for Motility Analysis of Caenorhabditis elegans |
title | Multi-Environment Model Estimation for Motility Analysis of Caenorhabditis elegans
|
title_full | Multi-Environment Model Estimation for Motility Analysis of Caenorhabditis elegans
|
title_fullStr | Multi-Environment Model Estimation for Motility Analysis of Caenorhabditis elegans
|
title_full_unstemmed | Multi-Environment Model Estimation for Motility Analysis of Caenorhabditis elegans
|
title_short | Multi-Environment Model Estimation for Motility Analysis of Caenorhabditis elegans
|
title_sort | multi-environment model estimation for motility analysis of caenorhabditis elegans |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2908547/ https://www.ncbi.nlm.nih.gov/pubmed/20661478 http://dx.doi.org/10.1371/journal.pone.0011631 |
work_keys_str_mv | AT sznitmanraphael multienvironmentmodelestimationformotilityanalysisofcaenorhabditiselegans AT guptamanaswi multienvironmentmodelestimationformotilityanalysisofcaenorhabditiselegans AT hagergregoryd multienvironmentmodelestimationformotilityanalysisofcaenorhabditiselegans AT arratiapauloe multienvironmentmodelestimationformotilityanalysisofcaenorhabditiselegans AT sznitmanjosue multienvironmentmodelestimationformotilityanalysisofcaenorhabditiselegans |