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A Generative Statistical Algorithm for Automatic Detection of Complex Postures.
- Source :
-
PLoS computational biology [PLoS Comput Biol] 2015 Oct 06; Vol. 11 (10), pp. e1004517. Date of Electronic Publication: 2015 Oct 06 (Print Publication: 2015). - Publication Year :
- 2015
-
Abstract
- This paper presents a method for automated detection of complex (non-self-avoiding) postures of the nematode Caenorhabditis elegans and its application to analyses of locomotion defects. Our approach is based on progressively detailed statistical models that enable detection of the head and the body even in cases of severe coilers, where data from traditional trackers is limited. We restrict the input available to the algorithm to a single digitized frame, such that manual initialization is not required and the detection problem becomes embarrassingly parallel. Consequently, the proposed algorithm does not propagate detection errors and naturally integrates in a "big data" workflow used for large-scale analyses. Using this framework, we analyzed the dynamics of postures and locomotion of wild-type animals and mutants that exhibit severe coiling phenotypes. Our approach can readily be extended to additional automated tracking tasks such as tracking pairs of animals (e.g., for mating assays) or different species.
- Subjects :
- Animals
Caenorhabditis elegans anatomy & histology
Computer Simulation
Data Interpretation, Statistical
Models, Statistical
Pattern Recognition, Automated methods
Reproducibility of Results
Sensitivity and Specificity
Algorithms
Caenorhabditis elegans physiology
Image Interpretation, Computer-Assisted methods
Locomotion physiology
Posture physiology
Whole Body Imaging methods
Subjects
Details
- Language :
- English
- ISSN :
- 1553-7358
- Volume :
- 11
- Issue :
- 10
- Database :
- MEDLINE
- Journal :
- PLoS computational biology
- Publication Type :
- Academic Journal
- Accession number :
- 26439258
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1004517