Deep Learning in Ultrasound

Deep learning is taking an ever more prominent role in medical imaging. On this page, we collect works that focus on applications of this powerful approach in ultrasound systems. Our goal is to provide researchers with an entry point to this growing field by collecting various works that demonstrate the impact of deep learning methodologies on many aspects of ultrasound imaging.

A complementary resource is the Challenge on Ultrasound Beamforming with Deep Learning (CUBDL) webpage. CUBL is offered as a component of the 2020 IEEE International Ultrasonics Symposium and is designed to explore the benefits of using deep learning for both focused and plane wave transmissions. The challenge is organized by Muyinatu Bell, Jiaqi (Justina) Huang, Dongwoon Hyun, Yonina Eldar, Ruud van Sloun and Massimo Mischi who are also maintaining the current page.
A recent special issue on deep learning in ultrasound imaging of the Journal of Biomedical and Health Informatics can be found here.

  • A unified deep network for beamforming and speckle reduction in plane wave imaging: A simulation study
    E. Mor, A. Bar-Hillel
    Ultrasonics (April 2020), volume 103, 106069, at Science Direct

  • Deep Neural Networks for Ultrasound Beamforming
    A. C. Luchies , B. C. Byram
    IEEE Transactions on Medical Imaging (September 2018), volume 37, 9, p. 2010-2021

  • Fast and accurate view classification of echocardiograms using deep learning
    A. Madani, R. Arnaout, M. Mofrad, R. Arnaout
    NPJ digital medicine (March 2018), 1, 6

  • Photoacoustic source detection and reflection artifact removal enabled by deep learning
    D. Allman, A. Reiter, M. A. L. Bell
    IEEE Transactions on Medical Imaging (June 2018), volume 37, 6, p. 1464-1477

  • 2D Ultrasound Imaging Based Intra-fraction Respiratory Motion Tracking for Abdominal Radiation Therapy Using Machine Learning
    P. Huang , L. Su, S. Chen, K. Cao, Q. Song, P. Kazanzides, I. Iordachita, M. A. L. Bell, J. W. Wong, D. Li, K. Ding
    Physics in Medicine and Biology (September 2019), 64, 18

  • Beamforming and Speckle Reduction Using Neural Networks
    D. Hyun, L. L. Brickson, K. T. Looby, J. J. Dahl
    IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (May 2019), volume 66, 5, p. 898-910

  • Deep Learning in Medical Ultrasound Analysis
    L. Shengfeng , W. Yi , Y. Xin, L. Baiying, L. Li, X. L. Shawn, N. Dong, W. Tianfo
    Engineering (April 2019), volume 5, 2, p. 261-275, at Science Direct

  • Deep Learning in Ultrasound Imaging
    R. J. G. van Sloun, R. Cohen, Y. C. Eldar, Proceedings of the IEEE, 2019

  • Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
    M. H. Hesamian, W. Jia, X. He, P. Kennedy
    Journal of Digital Imaging (May 2019), volume 32, 4, p. 582–596

  • Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound
    O. Solomon, R. Cohen, Y. Zhang, Y. Yang, H. Qiong, J. Luo, R. J. G. van Sloun and Y. C. Eldar,
    IEEE transactions on Medical Imaging (Early Access), November 2018

  • Adaptive Ultrasound Beamforming using Deep Learning
    B. Luijten, R. Cohen, F. J. de Bruijn, H. A. W. Schmeitz, M. Mischi, Y. C. Eldar, R. J.G. van Sloun, arXiv:1909.10342

  • Super-Resolution Ultrasound Localization Microscopy through Deep Learning
    R. J. G. van Sloun, O. Solomon, M. Bruce, Z. Z. Khaing, H. Wijkstra, Y. C. Eldar and M. Mischi, arXiv:1804.07661

  • End-to-End Learning-Based Ultrasound Reconstruction
    W. Simson, R. Göbl, M. Paschali, M. Krönke, K. Scheidhauer, W. Weber, N. Nava, arXiv:1904.04696

  • Universal Deep Beamformer for Variable Rate Ultrasound Imaging
    S. Khan, J. Huh, J. C. Ye, arXiv:1901.01706

  • Adaptive and Compressive Beamforming using Deep Learning for Medical Ultrasound
    S. Khan, J. Huh, J. C. Ye arXiv:1907.10257

  • Efficient B-mode ultrasound image reconstruction from sub-sampled RF data using deep learning
    T. H. Yoon, S. Khan, J. Huh, J. C. Ye, IEEE Transactions on Medical Imaging (2018), volume 38, 2, p. 325-336

  • To post new links or correct existing links, please e-mail SAMPL lab team member, Alon Mamistvalov at:

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