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Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

We have previously proposed temporal enhanced ultrasound (TeUS) as a new paradigm for tissue characterization. TeUS is based on analyzing a sequence of ultrasound data with deep learning and has been demonstrated to be successful for detection of cancer in ultrasound-guided prostate biopsy. Our aim is to enable the dissemination of this technology to the community for large-scale clinical validation.

Methods

In this paper, we present a unified software framework demonstrating near-real-time analysis of ultrasound data stream using a deep learning solution. The system integrates ultrasound imaging hardware, visualization and a deep learning back-end to build an accessible, flexible and robust platform. A client–server approach is used in order to run computationally expensive algorithms in parallel. We demonstrate the efficacy of the framework using two applications as case studies. First, we show that prostate cancer detection using near-real-time analysis of RF and B-mode TeUS data and deep learning is feasible. Second, we present real-time segmentation of ultrasound prostate data using an integrated deep learning solution.

Results

The system is evaluated for cancer detection accuracy on ultrasound data obtained from a large clinical study with 255 biopsy cores from 157 subjects. It is further assessed with an independent dataset with 21 biopsy targets from six subjects. In the first study, we achieve area under the curve, sensitivity, specificity and accuracy of 0.94, 0.77, 0.94 and 0.92, respectively, for the detection of prostate cancer. In the second study, we achieve an AUC of 0.85.

Conclusion

Our results suggest that TeUS-guided biopsy can be potentially effective for the detection of prostate cancer.

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Acknowledgements

This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and in part by the Canadian Institutes of Health Research (CIHR).

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Authors and Affiliations

  1. The University of British Columbia, Vancouver, BC, Canada

    Shekoofeh Azizi, Nathan Van Woudenberg, Samira Sojoudi & Purang Abolmaesumi

  2. National Institutes of Health, Bethesda, MD, USA

    Ming Li, Sheng Xu, Baris Turkbey, Peter Choyke, Peter Pinto & Bradford Wood

  3. Johns Hopkins University, Baltimore, MD, USA

    Emran M. Abu Anas

  4. Philips Research North America, Cambridge, MA, USA

    Amir Tahmasebi

  5. Sejong University, Gwangjin-gu, Seoul, Korea

    Jin Tae Kwak

  6. Queen’s University, Kingston, ON, Canada

    Parvin Mousavi

  7. Rensselaer Polytechnic Institute, Troy, NY, USA

    Pingkun Yan

Authors
  1. Shekoofeh Azizi
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  2. Nathan Van Woudenberg
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  3. Samira Sojoudi
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  4. Ming Li
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  5. Sheng Xu
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  11. Peter Choyke
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  13. Bradford Wood
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  14. Parvin Mousavi
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  15. Purang Abolmaesumi
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Corresponding author

Correspondence to Shekoofeh Azizi.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Azizi, S., Van Woudenberg, N., Sojoudi, S. et al. Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy. Int J CARS 13, 1201–1209 (2018). https://doi.org/10.1007/s11548-018-1749-z

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  • DOI: https://doi.org/10.1007/s11548-018-1749-z

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