Simulator-generated training datasets as an alternative to using patient data for machine learning: An example in myocardial segmentation with MRI.

Journal: Computer methods and programs in biomedicine
Published Date: Oct 27, 2020

Abstract

BACKGROUND AND OBJECTIVE: Supervised Machine Learning techniques have shown significant potential in medical image analysis. However, the training data that need to be collected for these techniques in the field of MRI 1) may not be available, 2) may be available but the size is small, 3) may be available but not representative and 4) may be available but with weak labels. The aim of this study was to overcome these limitations through advanced MR simulations on a realistic computer model of human anatomy without using a real MRI scanner, without scanning patients and without having personnel and the associated expenses.

Authors

  • Christos G Xanthis
    Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece; Department of Clinical Physiology, Clinical Sciences, Lund University and Lund University Hospital, Lund, Sweden. Electronic address: cxanthis@gmail.com.
  • Dimitrios Filos
    Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece. Electronic address: dimfilos@auth.gr.
  • Kostas Haris
    Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece. Electronic address: konharis@gmail.com.
  • Anthony H Aletras
    Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece; Department of Clinical Physiology, Clinical Sciences, Lund University and Lund University Hospital, Lund, Sweden. Electronic address: aletras@hotmail.com.

Keywords

External Resources

Popular Topics
Recent Journals