ArticleBiomedical engineering online2021
Multi-view 3D skin feature recognition and localization for patient tracking in spinal surgery applications.
Article in Biomedical engineering online, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Radiation-free real-time registration update for image-guided multilevel open posterior lumbar fusion: in vivo validation.North American Spine Society journal · 2026Article
- Autonomous Frameless Spine Registration and Robot-Assisted Surgery.Orthopaedic surgery · 2026Article
- Evolving Paradigms in Spinal Surgery: A Systematic Review of the Learning Curves in Minimally Invasive Spine Techniques.Neurospine · 2024Article
- Automatic image registration on intraoperative CBCT compared to Surface Matching registration on preoperative CT for spinal navigation: accuracy and workflow.International journal of computer assisted radiology and surgery · 2024Article
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Authors and funding
9 authors.
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Abstract
backgroundMinimally 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.
methodsMaximally 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.
resultsThe 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.
conclusionsThis 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.
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