Using deep learning to generate synthetic B-mode musculoskeletal ultrasound images.

Journal: Computer methods and programs in biomedicine
Published Date: Jun 4, 2020

Abstract

BACKGROUND AND OBJECTIVE: Deep learning approaches are common in image processing, but often rely on supervised learning, which requires a large volume of training images, usually accompanied by hand-crafted labels. As labelled data are often not available, it would be desirable to develop methods that allow such data to be compiled automatically. In this study, we used a Generative Adversarial Network (GAN) to generate realistic B-mode musculoskeletal ultrasound images, and tested the suitability of two automated labelling approaches.

Authors

  • Neil J Cronin
    Neuromuscular Research Centre, Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
  • Taija Finni
    Neuromuscular Research Centre, Faculty of Sport and Health Sciences, University of Jyvaskyla, Finland.
  • Olivier Seynnes
    Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway.

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