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International Symposium on Visual Computing

ISVC 2012: Advances in Visual Computing pp 104–114Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNIP,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.

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Author information

Authors and Affiliations

  1. Department of Computer Science, San Francisco State University, San Francisco, CA, 94132, USA

    Utsab Saha & Rahul Singh

  2. Open University Program, San Francisco State University, San Francisco, CA, 94132, USA

    Utsab Saha

Authors
  1. Utsab Saha
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  2. Rahul Singh
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, 89512, Reno, NV, USA

    Darko Koracin

  5. Department of Computer Science, University of California at Irvine, 92697-3435, Irvine, CA, USA

    Charless Fowlkes

  6. Eastman Kodak Company, 14650-2102, Rochester, NY, USA

    Sen Wang

  7. Department of Computer Science and Engineering, University of Colorado at Denver, 80217, Denver, CO, USA

    Min-Hyung Choi

  8. VRVis Zentrum für Virtual Reality and Visualisierung, 1220, Vienna, Austria

    Stephan Mantler

  9. California Institute for Telecommunications and Information Technology, University of California,,, San Diego, 92093, La Jolla, CA, USA

    Jürgen Schulze

  10. KAUST Visualizatioin Core Lab., 23955-6900, Thurwal, Saudi Arabia

    Daniel Acevedo

  11. Stony Brook University, 11794-4400, NY, USA

    Klaus Mueller

  12. Argonne National Laboratory, 60439, IL, USA

    Michael Papka

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© 2012 Springer-Verlag Berlin Heidelberg

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Saha, U., Singh, R. (2012). Vision-Based Tracking of Complex Macroparasites for High-Content Phenotypic Drug Screening. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33191-6_11

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  • DOI: https://doi.org/10.1007/978-3-642-33191-6_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33190-9

  • Online ISBN: 978-3-642-33191-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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