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3-D Rigid Point Set Registration for Computer-Assisted Orthopedic Surgery (CAOS): A Review From the Algorithmic Perspective

  • Zhe Min*
  • , Ang Zhang
  • , Zhengyan Zhang
  • , Jiaole Wang
  • , Shuang Song
  • , Hongliang Ren
  • , Max Q.H. Meng*
  • *Corresponding author for this work
  • Shandong University
  • University College London
  • Chinese University of Hong Kong
  • Hong Kong Polytechnic University
  • Harbin Institute of Technology Shenzhen
  • National University of Singapore
  • The Chinese University of Hong Kong, Shenzhen

Research output: Contribution to journalArticlepeer-review

Abstract

is an important problem in computer-assisted orthopedic surgery (CAOS). As one typical example, the pre-operative space where the patient-specific surgical plan is usually made needs to be accurately aligned with the patient space where the surgical procedures are conducted. The registration task still attracts a lot of research efforts, partially because establishing the point correspondences between two point sets (PSs) under noise, outliers and partial overlapping is a non-trivial problem. More specifically, in CAOS, (a) the intra-operative points only cover a small partial region of the pre-operative whole model; (b) the acquired intra-operative points are usually noisy and contains outliers. To facilitate the related researchers to quickly know both the classical and state-of-the-art rigid point set registration (RPSR) methods, we present a concise review about related rigid registration methods in CAOS in this paper. The contributions of this paper include: (1) we review the RPSR methods that are suitable for or very related to the CAOS application; (2) the surveyed registration algorithms' advantages and disadvantages are discussed and compared; (3) potential future research directions are also discussed.

Original languageEnglish
Pages (from-to)156-169
Number of pages14
JournalIEEE Transactions on Medical Robotics and Bionics
Volume5
Issue number2
DOIs
StatePublished - 1 May 2023
Externally publishedYes

Keywords

  • Review
  • anisotropic noise
  • computer-assisted orthopedic surgery (CAOS)
  • deep-learning
  • feature-based registration
  • maximum likelihood estimation problem
  • point set registration
  • surgical navigation

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