Abstract
Background
Minimally invasive spine surgery is dependent on accurate navigation. Computer-assisted navigation is increasingly used in minimally invasive surgery (MIS), but current solutions require the use of reference markers in the surgical field for both patient and instruments tracking.
Purpose
To improve reliability and facilitate clinical workflow, this study proposes a new marker-free tracking framework based on skin feature recognition.
Methods
Maximally Stable Extremal Regions (MSER) and Speeded Up Robust Feature (SURF) algorithms are applied for skin feature detection. The proposed tracking framework is based on a multi-camera setup for obtaining multi-view acquisitions of the surgical area. Features can then be accurately detected using MSER and SURF and afterward localized by triangulation. The triangulation error is used for assessing the localization quality in 3D.
Results
The framework was tested on a cadaver dataset and in eight clinical cases. The detected features for the entire patient datasets were found to have an overall triangulation error of 0.207 mm for MSER and 0.204 mm for SURF. The localization accuracy was compared to a system with conventional markers, serving as a ground truth. An average accuracy of 0.627 and 0.622 mm was achieved for MSER and SURF, respectively.
Conclusions
This study demonstrates that skin feature localization for patient tracking in a surgical setting is feasible. The technology shows promising results in terms of detected features and localization accuracy. In the future, the framework may be further improved by exploiting extended feature processing using modern optical imaging techniques for clinical applications where patient tracking is crucial.
Minimally invasive spine surgery is dependent on accurate navigation. Computer-assisted navigation is increasingly used in minimally invasive surgery (MIS), but current solutions require the use of reference markers in the surgical field for both patient and instruments tracking.
Purpose
To improve reliability and facilitate clinical workflow, this study proposes a new marker-free tracking framework based on skin feature recognition.
Methods
Maximally Stable Extremal Regions (MSER) and Speeded Up Robust Feature (SURF) algorithms are applied for skin feature detection. The proposed tracking framework is based on a multi-camera setup for obtaining multi-view acquisitions of the surgical area. Features can then be accurately detected using MSER and SURF and afterward localized by triangulation. The triangulation error is used for assessing the localization quality in 3D.
Results
The framework was tested on a cadaver dataset and in eight clinical cases. The detected features for the entire patient datasets were found to have an overall triangulation error of 0.207 mm for MSER and 0.204 mm for SURF. The localization accuracy was compared to a system with conventional markers, serving as a ground truth. An average accuracy of 0.627 and 0.622 mm was achieved for MSER and SURF, respectively.
Conclusions
This study demonstrates that skin feature localization for patient tracking in a surgical setting is feasible. The technology shows promising results in terms of detected features and localization accuracy. In the future, the framework may be further improved by exploiting extended feature processing using modern optical imaging techniques for clinical applications where patient tracking is crucial.
| Original language | English |
|---|---|
| Article number | 6 |
| Number of pages | 15 |
| Journal | BioMedical Engineering Online |
| Volume | 20 |
| DOIs | |
| Publication status | Published - 7 Jan 2021 |
Funding
We acknowledge Philips Electronics B.V., Best, The Netherlands and Karolinska University Hospital for the dataset, and the support on conducting this study. This work has been financed by the European H2020-ECSEL Joint Undertaking under Grant agreement n 692470, identified as ASTONISH project.
| Funders | Funder number |
|---|---|
| Karolinska University Hospital | |
| Philips Consumer Electronics |
Keywords
- Patient tracking
- Spinal surgery
- Skin tracking
- Surgical guidance
- Feature localization