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Page 6 of 8 Magaribuchi et al. Mini-invasive Surg 2024;8:6 https://dx.doi.org/10.20517/2574-1225.2023.81

feature points from the surgical field, capturing them as a 3D point cloud through Structure from Motion
[52]
(SfM), and tracking them, but no mention was made of registration with the 3D kidney model . Zhang
[53]
et al. used Semi-Global Block Matching (SGBM) to extract 3D point clouds from stereo image disparity .
They also used a deep learning method, Mask Region-based CNN (Mask R-CNN), for kidney segmentation
to assist with 3D point cloud extraction.

Other research exists that, while not strictly creating a 3D point cloud, captures the position and posture of
the kidney in real time using particle filtering and then registers it with the 3D model derived from CT .
[54]
This study compared it with registration via Coherent Point Drift (CPD), showing that particle filtering was
faster in terms of calculation speed in real-time intraoperative navigation.

Various studies have also been conducted on methods for creating deformable 3D models for high-
precision registration with deforming organs. One approach combined shape matching with cluster-based
deformation to create a non-rigid model . The effectiveness of this method was confirmed by creating a
[55]
model from a pig kidney captured by CT and conducting experiments. Another study used a finite element
method (FEM) model as a non-rigid model, reproducing organ deformation and movement in conjunction
[41]
with the positional change of fiducial markers attached to the kidney .

CONCLUSION
Numerous studies have aimed to achieve high-precision 3D navigation, and some attempts in actual
surgeries have yielded certain effects. However, none have led to a complete solution that could be widely
generalized. Further advancements in 3D navigation research capable of accommodating deforming organs
are expected to contribute to surgical procedures in the future.

DECLARATIONS
Authors’ contributions
Made substantial contributions to conception and design of the study and performed data analysis and
interpretation: Magaribuchi T
Managed acquired data and drafted the manuscript: Masui K, Goto T, Saito R, Kobayashi T

Availability of data and materials
Not applicable.

Financial support and sponsorship
None.

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2024.