Abdominal Radiology

, Volume 43, Issue 4, pp 786–799 | Cite as

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

  • Laura J. Brattain
  • Brian A. Telfer
  • Manish Dhyani
  • Joseph R. Grajo
  • Anthony E. Samir
Invited article


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.


Deep learning Elastography Machine learning Medical ultrasound Sonography 



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.

Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Laura J. Brattain
    • 1
  • Brian A. Telfer
    • 1
  • Manish Dhyani
    • 2
    • 4
  • Joseph R. Grajo
    • 3
  • Anthony E. Samir
    • 4
  1. 1.MIT Lincoln LaboratoryLexingtonUSA
  2. 2.Department of Internal MedicineSteward Carney HospitalBostonUSA
  3. 3.Department of Radiology, Division of Abdominal ImagingUniversity of Florida College of MedicineGainesvilleUSA
  4. 4.Division of Ultrasound, Department of Radiology, Center for Ultrasound Research & TranslationMassachusetts General HospitalBostonUSA

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