Automatic markerless registration and tracking of the bone for computer-assisted orthopaedic surgery
File(s)
Published version
Author(s)
Liu, He
Rodriguez y Baena, Ferdinando
Type
Journal Article
Abstract
To achieve a simple and less invasive registration procedure in computer-assisted orthopaedic surgery, we propose an automatic, markerless registration and tracking method based on depth imaging and deep learning. A depth camera is used to continuously capture RGB and depth images of the exposed bone during surgery, and deep neural networks are trained to first localise the surgical target using the RGB image, then segment the target area of the corresponding depth image, from which the surface geometry of the target bone can be extracted. The extracted surface is then compared to a pre-operative model of the same bone for registration. This process can be performed dynamically during the procedure at a rate of 5-6 Hz, without any need for surgeon intervention or invasive optical markers. Ex vivo registration experiments were performed on a cadaveric knee, and accuracy measurements against an optically tracked ground truth resulted in a mean translational error of 2.74 mm and a mean rotational error of 6.66°. Our results are the first to describe a promising new way to achieve automatic markerless registration and tracking in computer-assisted orthopaedic surgery, demonstrating that truly seamless registration and tracking of the limb is within reach. Our method reduces invasiveness by removing the need for percutaneous markers. The surgeon is also exempted from inserting markers and collecting registration points manually, which contributes to a more efficient surgical workflow and shorter procedure time in the operating room.
Date Issued
2020-02-28
Date Acceptance
2020-02-15
Citation
IEEE Access, 2020, 8 (1), pp.42010-42020
ISSN
2169-3536
Publisher
Institute of Electrical and Electronics Engineers
Start Page
42010
End Page
42020
Journal / Book Title
IEEE Access
Volume
8
Issue
1
Copyright Statement
© 2020 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
Identifier
https://ieeexplore.ieee.org/document/9018195
Subjects
Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Surgery
Bones
Image segmentation
Cameras
Optical imaging
Deep learning
Computer-assisted orthopaedic surgery
deep learning
depth imaging
markerless registration
NAVIGATION
08 Information and Computing Sciences
09 Engineering
10 Technology
Publication Status
Published
Date Publish Online
2020-02-28