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.
Highlights
Registration plays an important role in computer-assisted orthopaedic surgery, as it defines the position of the patient with respect to the surgical system so that a pre-operative plan can be correctly aligned with the surgical site
The registration result provides the pose of the target femur in the depth camera reference frame, which is transformed into the Atracsys reference frame for comparison with the ground truth, i.e. the pose of the femur scanned by the probe
This study proposes a depth image segmentation method based on deep learning, which can be used to achieve automatic markerless registration and tracking of the limb for knee surgery
Summary
Registration plays an important role in computer-assisted orthopaedic surgery, as it defines the position of the patient with respect to the surgical system so that a pre-operative plan can be correctly aligned with the surgical site. Model can be morphed onto it for intra-operative planning purposes, avoiding the need for costly pre-operative imaging. Both registration methods can be defined as ‘static’, because the registration is only performed once. During surgery, the bone will inevitably move, either by the surgeon to adjust the cutting position (in the cm range), or due to cutting or tissue retraction forces (in the mm range). These movements, small, will cause an error in bone resection if not accounted for. The active robotic system ROBODOC (Curexo Technology, Inc.) employs limb fixation, whereas the semi-active orthopaedic robots Mako (Stryker Corp.), Navio (Smith & Nephew PLC) and ROSA Knee
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
