A simultaneous localization and tracking method for a worm tracking system

Mateusz Kowalski; Piotr Kaczmarek; Rafał Kabaciński; Mieszko Matuszczak; Kamil Tranbowicz; Robert Sobkowiak

International Journal of Applied Mathematics and Computer Science (2014)

  • Volume: 24, Issue: 3, page 599-609
  • ISSN: 1641-876X

Abstract

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The idea of worm tracking refers to the path analysis of Caenorhabditis elegans nematodes and is an important tool in neurobiology which helps to describe their behavior. Knowledge about nematode behavior can be applied as a model to study the physiological addiction process or other nervous system processes in animals and humans. Tracking is performed by using a special manipulator positioning a microscope with a camera over a dish with an observed individual. In the paper, the accuracy of a nematode's trajectory reconstruction is investigated. Special attention is paid to analyzing errors that occurred during the microscope displacements. Two sources of errors in the trajectory reconstruction are shown. One is due to the difficulty in accurately measuring the microscope shift, the other is due to a nematode displacement during the microscope movement. A new method that increases path reconstruction accuracy based only on the registered sequence of images is proposed. The method Simultaneously Localizes And Tracks (SLAT) the nematodes, and is robust to the positioning system displacement errors. The proposed method predicts the nematode position by using NonParametric Regression (NPR). In addition, two other methods of the SLAT problem are implemented to evaluate the NPR method. The first consists in ignoring the nematode displacement during microscope movement, and the second is based on a Kalman filter. The results suggest that the SLAT method based on nonparametric regression gives the most promising results and decreases the error of trajectory reconstruction by 25% compared with reconstruction based on data from the positioning system.

How to cite

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Mateusz Kowalski, et al. "A simultaneous localization and tracking method for a worm tracking system." International Journal of Applied Mathematics and Computer Science 24.3 (2014): 599-609. <http://eudml.org/doc/271901>.

@article{MateuszKowalski2014,
abstract = {The idea of worm tracking refers to the path analysis of Caenorhabditis elegans nematodes and is an important tool in neurobiology which helps to describe their behavior. Knowledge about nematode behavior can be applied as a model to study the physiological addiction process or other nervous system processes in animals and humans. Tracking is performed by using a special manipulator positioning a microscope with a camera over a dish with an observed individual. In the paper, the accuracy of a nematode's trajectory reconstruction is investigated. Special attention is paid to analyzing errors that occurred during the microscope displacements. Two sources of errors in the trajectory reconstruction are shown. One is due to the difficulty in accurately measuring the microscope shift, the other is due to a nematode displacement during the microscope movement. A new method that increases path reconstruction accuracy based only on the registered sequence of images is proposed. The method Simultaneously Localizes And Tracks (SLAT) the nematodes, and is robust to the positioning system displacement errors. The proposed method predicts the nematode position by using NonParametric Regression (NPR). In addition, two other methods of the SLAT problem are implemented to evaluate the NPR method. The first consists in ignoring the nematode displacement during microscope movement, and the second is based on a Kalman filter. The results suggest that the SLAT method based on nonparametric regression gives the most promising results and decreases the error of trajectory reconstruction by 25% compared with reconstruction based on data from the positioning system.},
author = {Mateusz Kowalski, Piotr Kaczmarek, Rafał Kabaciński, Mieszko Matuszczak, Kamil Tranbowicz, Robert Sobkowiak},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {Caenorhabditis elegans behavior; worm tracking; computer vision; image processing; feature extraction},
language = {eng},
number = {3},
pages = {599-609},
title = {A simultaneous localization and tracking method for a worm tracking system},
url = {http://eudml.org/doc/271901},
volume = {24},
year = {2014},
}

TY - JOUR
AU - Mateusz Kowalski
AU - Piotr Kaczmarek
AU - Rafał Kabaciński
AU - Mieszko Matuszczak
AU - Kamil Tranbowicz
AU - Robert Sobkowiak
TI - A simultaneous localization and tracking method for a worm tracking system
JO - International Journal of Applied Mathematics and Computer Science
PY - 2014
VL - 24
IS - 3
SP - 599
EP - 609
AB - The idea of worm tracking refers to the path analysis of Caenorhabditis elegans nematodes and is an important tool in neurobiology which helps to describe their behavior. Knowledge about nematode behavior can be applied as a model to study the physiological addiction process or other nervous system processes in animals and humans. Tracking is performed by using a special manipulator positioning a microscope with a camera over a dish with an observed individual. In the paper, the accuracy of a nematode's trajectory reconstruction is investigated. Special attention is paid to analyzing errors that occurred during the microscope displacements. Two sources of errors in the trajectory reconstruction are shown. One is due to the difficulty in accurately measuring the microscope shift, the other is due to a nematode displacement during the microscope movement. A new method that increases path reconstruction accuracy based only on the registered sequence of images is proposed. The method Simultaneously Localizes And Tracks (SLAT) the nematodes, and is robust to the positioning system displacement errors. The proposed method predicts the nematode position by using NonParametric Regression (NPR). In addition, two other methods of the SLAT problem are implemented to evaluate the NPR method. The first consists in ignoring the nematode displacement during microscope movement, and the second is based on a Kalman filter. The results suggest that the SLAT method based on nonparametric regression gives the most promising results and decreases the error of trajectory reconstruction by 25% compared with reconstruction based on data from the positioning system.
LA - eng
KW - Caenorhabditis elegans behavior; worm tracking; computer vision; image processing; feature extraction
UR - http://eudml.org/doc/271901
ER -

References

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  1. Bączyk, R. and Kasiński, A. (2010). Visual simultaneous localisation and map-building supported by structured landmarks, International Journal of Applied Mathematics and Computer Science 20(2): 281-293, DOI: 10.2478/v10006010-0021-7. Zbl1196.93053
  2. Baek, J.-H., Cosman, P., Feng, Z., Silver, J. and Schafer, W.R. (2002). Using machine vision to analyze and classify Caenorhabditis elegans behavioral phenotypes quantitatively, Journal of Neuroscience Methods 118(1): 9-21. 
  3. Byrd, R., Gilbert, J.C. and Nocedal, J. (2000). A trust region method based on interior point techniques for nonlinear programming, Mathematical Programming 89(1): 149-185. Zbl1033.90152
  4. CESC (1988). Genome sequence of the nematode C. elegans: A platform for investigating biology, Science 282(5396): 2012-2018. 
  5. de Bono, M. and Bargmann, C.I. (1998). Natural variation in a neuropeptide Y receptor homolog modifies social behavior and food response in C. elegans, Cell 94(5): 679-689. 
  6. de Boor, C. (1978). A Practical Guide to Splines, Springer, New York, NY. Zbl0406.41003
  7. Feng, Z., Cronin, C., Wittig, J., Sternberg, P. and Schafer, W. (2004). An imaging system for standardized quantitative analysis of C. elegans behavior, BMC Bioinformatics 5(1): 115. 
  8. Fitzgibbon, A. (2001). Simultaneous linear estimation of multiple view geometry and lens distortion, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, Kauai, HI, USA, Vol. 1, pp. I-125-I-132. 
  9. Geng, W., Cosman, P., Berry, C., Feng, Z. and Schafer, W. R. (2004). Automatic tracking, feature extraction and classification of C. elegans phenotypes, IEEE Transactions on Biomedical Engineering 51(10): 1811-1820. 
  10. Gray, J.M., Hill, J.J. and Bargmann, C.I. (2005). A circuit for navigation in Caenorhabditis elegans, Proceedings of the National Academy of Sciences of the United States of America 102(9): 3184-3191. 
  11. Hardaker, L.A., Singer, E., Kerr, R., Zhou, G. and Schafer, W.R. (2001). Serotonin modulates locomotory behavior and coordinates egg-laying and movement in Caenorhabditis elegans, Journal of Neurobiology 49(4): 303-313. 
  12. Hoshi, K. and Shingai, R. (2006). Computer-driven automatic identification of locomotion states in Caenorhabditis elegans, Journal of Neuroscience Methods 157(2): 355-363. 
  13. Husson, S.J., Costa, W.S., Schmitt, C. and Gottschalk, A. (2012). Keeping track of worm trackers, http://www.wormbook.org/chapters/www_tracking/tracking.html. 
  14. Jorgensen, E.M. and Mango, S.E. (2002). The art and design of genetic screens: Caenorhabditis elegans, Nature Reviews Genetics 3(5): 356-369. 
  15. Kalman, R.E. (1960). A new approach to linear filtering and prediction problems, Transactions of the ASME Journal of Basic Engineering D 82: 35-45. 
  16. MRC-LMB (2012). Part list for a single tracking unit, http://www.mrc-lmb.cam.ac.uk/wormtracker/webcontent/TrackerUnitPartList.pdf. 
  17. Nass, R., Miller III, D.M. and Blakely, R.D. (2001). C. elegans: A novel pharmacogenetic model to study Parkinson's disease, Parkinsonism and Related Disorders 7(3): 185-191. 
  18. Otsu, N. (1979). A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics 9(1): 62-66. 
  19. Perez, A. and Gonzalez, R.C. (1987). An iterative thresholding algorithm for image segmentation, IEEE Transactions on Pattern Analysis And Machine Intelligence 9(6): 742-751. 
  20. Pierce-Shimomura, J.T., Morse, T.M. and Lockery, S.R. (1999). The fundamental role of pirouettes in Caenorhabditis elegans chemotaxis, The Journal of Neuroscience 19(21): 9557-9569. 
  21. Ramot, D., Johnson, B.E., Berry, Jr, T.L., Carnell, L. and Goodman, M. B. (2008). The parallel worm tracker: A platform for measuring average speed and drug-induced paralysis in nematodes, PLoS ONE 3(5): e2208. 
  22. Rankin, C. H. (2002). From gene to identified neuron to behaviour in Caenorhabditis elegans, Nature Reviews Genetics 3(8): 622-630. 
  23. Roussel, N., Morton, C., Finger, F. and Roysam, B. (2007). A computational model for C. elegans locomotory behavior: Application to multiworm tracking, IEEE Transactions on Biomedical Engineering 54(10): 1786-1797. 
  24. Ségalat, L., Grisoni, K., Archer, J., Vargas, C., Bertrand, A. and Anderson, J. (2005). Capon expression in skeletal muscle is regulated by position, repair, NOS activity, and dystrophy, Experimental Cell Research 302(2): 170-179. 
  25. Skrzypczyński, P. (2009). Simultaneous localization and mapping: A feature-based probabilistic approach, International Journal of Applied Mathematics and Computer Science 19(4): 575-588, DOI: 10.2478/v10006-009-0045-z. Zbl1300.93157
  26. Sobkowiak, R., Kowalski, M. and Lesicki, A. (2011). Concentration- and time-dependent behavioral changes in Caenorhabditis elegans after exposure to nicotine, Pharmacology Biochemistry and Behavior 99(3): 365-370. 
  27. Stiernagle, T. (2006). Maintenance of C. elegans, http://www.wormbook.org/chapters/www_strainmaintain/strainmaintain.html. 
  28. Waggoner, L.E., Zhou, G., Schafer, R.W. and Schafer, W.R. (1998). Control of alternative behavioral states by serotonin in Caenorhabditis elegans, Neuron 21(1): 203-214. 
  29. White, J.G., Southgate, E., Thomson, J.N. and Brenner, S. (1986). The structure of the nervous system of the nematode Caenorhabditis elegans, Philosophical Transactions of the Royal Society 314(1165): 1-340. 
  30. Wu, Y., Luo, Y., Wu, Z., Butko, P., Christen, Y., Lambert, M.P., Klein, W.L. and Link, C.D. (2006). Amyloid-β-induced pathological behaviors are suppressed by Ginkgo biloba extract EGb 761 and ginkgolides in transgenic Caenorhabditis elegans, Journal of Neuroscience 26(50): 13102-13113. 

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