Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology.

Journal: Radiation oncology (London, England)
Published Date: May 21, 2024

Abstract

PURPOSE: Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensional (3D) convolutional U-Net based deformable image registration (ConvUNet-DIR) method using unsupervised learning to establish correspondence between baseline pre-operative and follow-up MRI scans of patients with brain glioma.

Authors

  • Alexander F I Osman
    Department of Radiation Oncology, American University of Beirut Medical Center, Riad El-Solh, 1107 2020, Beirut, Lebanon.
  • Kholoud S Al-Mugren
    Department of Physics, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
  • Nissren M Tamam
    Department of Physics, College of Science, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia.
  • Bilal Shahine
    Department of Radiation Oncology, American University of Beirut Medical Center, Beirut, Lebanon.

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