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Machine learning for medical ultrasound: status, methods, and future opportunities

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Abstract

Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.

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Acknowledgments

This material is based upon work supported by the Assistant Secretary of Defense for Research and Engineering under Air Force Contract No. FA8721-05-C-0002 and/or FA8702-15-D-0001. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Assistant Secretary of Defense for Research and Engineering. This work is also supported by the NIBIB of the National Institutes of Health under award numbers HHSN268201300071 C and K23 EB020710. The authors are solely responsible for the content and the work does not represent the official views of the National Institutes of Health.

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Authors and Affiliations

  1. MIT Lincoln Laboratory, 244 Wood St, Lexington, MA, 02420, USA

    Laura J. Brattain & Brian A. Telfer

  2. Department of Internal Medicine, Steward Carney Hospital, Boston, MA, 02124, USA

    Manish Dhyani

  3. Department of Radiology, Division of Abdominal Imaging, University of Florida College of Medicine, Gainesville, FL, USA

    Joseph R. Grajo

  4. Division of Ultrasound, Department of Radiology, Center for Ultrasound Research & Translation, Massachusetts General Hospital, Boston, MA, 02114, USA

    Manish Dhyani & Anthony E. Samir

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  1. Laura J. Brattain
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  2. Brian A. Telfer
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  3. Manish Dhyani
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  4. Joseph R. Grajo
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  5. Anthony E. Samir
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Correspondence to Laura J. Brattain.

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Brattain, L.J., Telfer, B.A., Dhyani, M. et al. Machine learning for medical ultrasound: status, methods, and future opportunities. Abdom Radiol 43, 786–799 (2018). https://doi.org/10.1007/s00261-018-1517-0

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