A multi-scale unsupervised learning for deformable image registration.

Journal: International journal of computer assisted radiology and surgery
Published Date: Oct 22, 2021

Abstract

PURPOSE: Image registration is a fundamental task in the area of image processing, and it is critical to many clinical applications, e.g., computer-assisted surgery. In this work, we attempt to design an effective framework that gains higher accuracy at a minimal cost of the invertibility of registration field.

Authors

  • Shuwei Shao
    School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.
  • Zhongcai Pei
    School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
  • Weihai Chen
    School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
  • Wentao Zhu
    Department of Computer Science, University of California, Irvine, CA, USA.
  • Xingming Wu
    School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.
  • Baochang Zhang

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