Target-oriented deep learning-based image registration with individualized test-time adaptation.

Journal: Medical physics
Published Date: May 24, 2023

Abstract

BACKGROUND: A classic approach in medical image registration is to formulate an optimization problem based on the image pair of interest, and seek a deformation vector field (DVF) to minimize the corresponding objective, often iteratively. It has a clear focus on the targeted pair, but is typically slow. In contrast, more recent deep-learning-based registration offers a much faster alternative and can benefit from data-driven regularization. However, learning is a process to "fit" the training cohort, whose image or motion characteristics or both may differ from the pair of images to be tested, which is the ultimate goal of registration. Therefore, generalization gap poses a high risk with direct inference alone.

Authors

  • Yudi Sang
    Department of Bioengineering, University of California, Los Angeles, California, USA.
  • Michael McNitt-Gray
    Departments of Biomedical Physics and Radiology, University of California, Los Angeles, CA, 90095, USA.
  • Yingli Yang
    Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, CA, USA.
  • Minsong Cao
    Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, 90095, USA.
  • Daniel Low
    University of California, Los Angeles, CA, 90024, USA.
  • Dan Ruan
    Departments of Radiation Oncology, Biomedical Physics and Bioengineering, UCLA, Los Angeles, CA, 90095, USA.

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