Literature DB >> 17945791

Automated tracking of multiple C. Elegans.

Ebraheem Fontaine1, Joel Burdick, Alan Barr.   

Abstract

This paper presents a method for model based automated tracking of multiple worm-like creatures. These methods are essential for accurate quantitative analysis into the genetic basis of behavior that involve more than one organism. An accurate worm model is designed using the geometry of planar curves and nonlinear estimation of the model's parameters are performed using a central difference Kalman filter (CDKF). The filter can naturally be expanded to estimate the locations of multiple worms and determine when they are occluding each other. The predicted location of the models at each iteration allows for an efficient method to determine the regions that are undergoing occlusions. Experiments on actual C. Elegans mating sequence data demonstrate the quality of the proposed method.

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Mesh:

Year:  2006        PMID: 17945791     DOI: 10.1109/IEMBS.2006.260657

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  12 in total

Review 1.  Strategies for automated analysis of C. elegans locomotion.

Authors:  Steven D Buckingham; David B Sattelle
Journal:  Invert Neurosci       Date:  2008-08-08

2.  Profiling a Caenorhabditis elegans behavioral parametric dataset with a supervised K-means clustering algorithm identifies genetic networks regulating locomotion.

Authors:  Shijie Zhang; Wei Jin; Ying Huang; Wei Su; Jiong Yang; Zhaoyang Feng
Journal:  J Neurosci Methods       Date:  2011-03-03       Impact factor: 2.390

3.  Robust tracking and quantification of C. elegans body shape and locomotion through coiling, entanglement, and omega bends.

Authors:  Nicolas Roussel; Jeff Sprenger; Susan J Tappan; Jack R Glaser
Journal:  Worm       Date:  2015-01-22

4.  Image enhancement for tracking the translucent larvae of Drosophila melanogaster.

Authors:  Sukant Khurana; Wen-Ke Li; Nigel S Atkinson
Journal:  PLoS One       Date:  2010-12-30       Impact factor: 3.240

5.  High-throughput behavioral analysis in C. elegans.

Authors:  Nicholas A Swierczek; Andrew C Giles; Catharine H Rankin; Rex A Kerr
Journal:  Nat Methods       Date:  2011-06-05       Impact factor: 28.547

6.  A Generative Statistical Algorithm for Automatic Detection of Complex Postures.

Authors:  Stanislav Nagy; Marc Goessling; Yali Amit; David Biron
Journal:  PLoS Comput Biol       Date:  2015-10-06       Impact factor: 4.475

7.  Active backlight for automating visual monitoring: An analysis of a lighting control technique for Caenorhabditis elegans cultured on standard Petri plates.

Authors:  Joan Carles Puchalt; Antonio-José Sánchez-Salmerón; Patricia Martorell Guerola; Salvador Genovés Martínez
Journal:  PLoS One       Date:  2019-04-16       Impact factor: 3.240

8.  Motion prediction enables simulated MR-imaging of freely moving model organisms.

Authors:  Markus Reischl; Mazin Jouda; Neil MacKinnon; Erwin Fuhrer; Natalia Bakhtina; Andreas Bartschat; Ralf Mikut; Jan G Korvink
Journal:  PLoS Comput Biol       Date:  2019-12-19       Impact factor: 4.475

9.  CeleST: computer vision software for quantitative analysis of C. elegans swim behavior reveals novel features of locomotion.

Authors:  Christophe Restif; Carolina Ibáñez-Ventoso; Mehul M Vora; Suzhen Guo; Dimitris Metaxas; Monica Driscoll
Journal:  PLoS Comput Biol       Date:  2014-07-17       Impact factor: 4.475

10.  Automated Planar Tracking the Waving Bodies of Multiple Zebrafish Swimming in Shallow Water.

Authors:  Shuo Hong Wang; Xi En Cheng; Zhi-Ming Qian; Ye Liu; Yan Qiu Chen
Journal:  PLoS One       Date:  2016-04-29       Impact factor: 3.240

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