Generating Synthetic Labeled Data From Existing Anatomical Models: An Example With Echocardiography Segmentation.

Journal: IEEE transactions on medical imaging
Published Date:

Abstract

Deep learning can bring time savings and increased reproducibility to medical image analysis. However, acquiring training data is challenging due to the time-intensive nature of labeling and high inter-observer variability in annotations. Rather than labeling images, in this work we propose an alternative pipeline where images are generated from existing high-quality annotations using generative adversarial networks (GANs). Annotations are derived automatically from previously built anatomical models and are transformed into realistic synthetic ultrasound images with paired labels using a CycleGAN. We demonstrate the pipeline by generating synthetic 2D echocardiography images to compare with existing deep learning ultrasound segmentation datasets. A convolutional neural network is trained to segment the left ventricle and left atrium using only synthetic images. Networks trained with synthetic images were extensively tested on four different unseen datasets of real images with median Dice scores of 91, 90, 88, and 87 for left ventricle segmentation. These results match or are better than inter-observer results measured on real ultrasound datasets and are comparable to a network trained on a separate set of real images. Results demonstrate the images produced can effectively be used in place of real data for training. The proposed pipeline opens the door for automatic generation of training data for many tasks in medical imaging as the same process can be applied to other segmentation or landmark detection tasks in any modality. The source code and anatomical models are available to other researchers. https://adgilbert.github.io/data-generation/.

Authors

  • Andrew Gilbert
    GE Vingmed Ultrasound AS, Horton, Norway.
  • Maciej Marciniak
    Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
  • Cristobal Rodero
    Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.
  • Pablo Lamata
    Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's Health Partners, King's College of London, 3rd Floor Lambeth Wing, St Thomas' Hospital, SE1 7EH, London.
  • Eigil Samset
  • Kristin McLeod
    GE Vingmed Ultrasound AS, Horton, Norway.