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 language | English |
|---|---|
| Pages (from-to) | 156-169 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Medical Robotics and Bionics |
| Volume | 5 |
| Issue number | 2 |
| DOIs | |
| State | Published - 1 May 2023 |
| Externally published | Yes |
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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