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Towards markerless computer assisted surgery: Application to total knee arthroplasty.

Salaheddine Sta^1,2, Jérôme Ogor², Hoel Letissier^2,3,4

¹IMT-Atlantique Bretagne- Pays de la Loire, Brest, France.

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|June 4, 2021

Summary

This study introduces a novel depth-sensor based method for markerless surgical instrument localization in Computer Assisted Orthopaedic Surgery (CAOS). The Microsoft Kinect Azure and Occipital Structure core sensors showed distinct advantages in performance metrics.

Keywords:

6-DoF pose estimation arthroplasty computer assisted orthopedic surgery depth sensor point pair features total knee arthroplasty

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Area of Science:

Orthopaedic Surgery
Computer Assisted Surgery
Surgical Instrument Localization

Background:

Conventional Computer Assisted Orthopaedic Surgery (CAOS) solutions face limitations in surgical instrument localization.
Accurate real-time tracking of surgical instruments is crucial for enhancing precision in orthopaedic procedures.

Purpose of the Study:

To propose and evaluate a novel depth-sensor based approach for markerless localization of surgical instruments in orthopaedics.
To overcome the limitations of existing CAOS localization techniques.
To compare the performance of different depth sensors for this application.

Main Methods:

The Point-Pair Features (PPF) algorithm was employed for 6D pose estimation of Patient-Specific Instruments (PSIs) during Total Knee Arthroplasty (TKA).
Four different depth sensors were evaluated.
Performance was assessed using Depth Fitting Error (DFE), Pose Errors, and Success Rate metrics.

Main Results:

The Microsoft Kinect Azure demonstrated superior performance regarding Depth Fitting Error (DFE).
The Occipital Structure core exhibited better results in terms of Pose Errors and Success Rate.
Comparative analysis revealed sensor-specific strengths for instrument localization.

Conclusions:

This research presents the first depth-sensor based solution for intraoperative markerless localization of surgical instruments in orthopaedic surgery.
The findings highlight the potential of depth sensing technology to advance CAOS.
The study provides valuable insights for selecting appropriate depth sensors for surgical applications.