Advertisement

Vision-Based Tracking of Complex Macroparasites for High-Content Phenotypic Drug Screening

  • Utsab Saha
  • Rahul Singh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7432)

Abstract

This paper proposes a method for vision-based automated tracking of schistosomula, the etiological agent of schistosomiasis, a disease which affects over 200 million people worldwide. The proposed tracking system is intended to facilitate high-throughput and high-content drug screening against the schistosomula by taking into account their complex phenotypic response to different candidate drug molecules. Our method addresses the unique challenges in tracking schistosomula, which include temporal changes in morphology, appearance, and motion characteristics due to the effect of drugs, as well as behavioral specificities of the parasites such as their tendency to remain stagnant in dense clusters followed by sudden rapid fluctuations in size and shape of individuals. Such issues are difficult to address using current bio-image tracking systems that have predominantly been developed for tracking simpler cell movements. We also propose a novel method for utilizing the results of tracking to improve the accuracy of segmentation across all images of the video sequence. Experiments demonstrate the efficacy of the proposed tracking method.

Keywords

Video Sequence Motion Characteristic Segmentation Error Refinement Level Parasite Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Nwaka, S., Hudson, A.: Innovative lead discovery strategies for tropical diseases. Nat. Rev. Drug Discov. 5, 941–955 (2006)CrossRefGoogle Scholar
  2. 2.
    Lee, H., et al.: Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis. BMC Genomics 12(suppl. 1), S4 (2012)Google Scholar
  3. 3.
    Geng, W., Cosman, P., Berry, C.C., Feng, Z., Schafer, W.R.: Automatic track-ing, feature extraction and classification of C. elegans phenotypes. IEEE Transactions on Biomedical Engineering 51(10), 1811–1820 (2004)CrossRefGoogle Scholar
  4. 4.
    Baek, J.-H., Cosman, P., Feng, Z., Silver, J., Schafer, W.R.: Using machine vision to analyze and classify Caenorhabditis elegans behavior phenotypes quantitatively. Journal of Neuroscience Methods 118(1), 9–21 (2002)CrossRefGoogle Scholar
  5. 5.
    Masoud, O., Papanikolopoulos, N.: A novel method for tracking and counting pedestrians in real-time using a single camera. IEEE Trans Vehicular Technology 50(5), 1267–1278 (2001)CrossRefGoogle Scholar
  6. 6.
    Li, K., Miller, E.D., Chen, M., Kanade, T., Weiss, L.E., Campbell, P.G.: Cell population tracking and lineage construction with spatiotemporal context. Med. Image Anal. 12(5), 546–566 (2008)CrossRefGoogle Scholar
  7. 7.
    Singh, R., Pittas, M., Heskia, I., Xu, F., McKerrow, J.H., Caffrey, C.: Automated Image-Based Phenotypic Screening for High-Throughput Drug Discovery. In: IEEE Symposium on Computer-Based Medical Systems, pp. 1–8 (2009)Google Scholar
  8. 8.
    Al-Kofahi, O., Radke, R.J., Goderie, S.K., Shen, Q., Temple, S., Roysam, B.N.: Automated Cell Lineage Construction: A Rapid Method to Analyze Clonal Development Established with Murine Neural Progenitor Cells. Cell Cycle 5(3), 327–335 (2006)CrossRefGoogle Scholar
  9. 9.
    Blake, A., Isard, M.: Condensation – conditional density propagation for visual tracking. International Journal of Computer Vision 28(1), 5–28 (1998)CrossRefGoogle Scholar
  10. 10.
    Heisele, B., Kressel, U., Ritter, W.: Tracking nonrigid, moving objects based on color cluster flow. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 257–260 (June 1997)Google Scholar
  11. 11.
    Moody-Davis, A., Mennillo, L., Singh, R.: Region-Based Segmentation of Parasites for High-throughput Screening. In: Bebis, G. (ed.) ISVC 2011, Part I. LNCS, vol. 6938, pp. 43–53. Springer, Heidelberg (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Utsab Saha
    • 1
    • 2
  • Rahul Singh
    • 1
  1. 1.Department of Computer ScienceSan Francisco State UniversitySan FranciscoUSA
  2. 2.Open University ProgramSan Francisco State UniversitySan FranciscoUSA

Personalised recommendations