ArticlePLoS computational biology2015
A Generative Statistical Algorithm for Automatic Detection of Complex Postures.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- Automatic worm detection to solve overlapping problems using a convolutional neural network.Scientific reports · 2022Article
- Megapixel camera arrays enable high-resolution animal tracking in multiwell plates.Communications biology · 2022Article
- TheFrontiers in aging · 2022Article
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- WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans.PLoS computational biology · 2021Article
- High-throughput behavioral screen in C. elegans reveals Parkinson's disease drug candidates.Communications biology · 2021Article
- Improving skeleton algorithm for helping Caenorhabditis elegans trackers.Scientific reports · 2020Article
- Quantitative imaging of sleep behavior in Caenorhabditis elegans and larval Drosophila melanogaster.Nature protocols · 2019Article
- Powerful and interpretable behavioural features for quantitative phenotyping ofPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2018Article
- Measuring behavior across scales.BMC biology · 2018Review
- Inactivity periods and postural change speed can explain atypical postural change patterns of Caenorhabditis elegans mutants.BMC bioinformatics · 2017Article
- A network approach to discerning the identities of C. elegans in a free moving population.Scientific reports · 2016Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.