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A Marker-Less Registration Approach for Mixed Reality–Aided Maxillofacial Surgery: a Pilot Evaluation

  • Antonio PepeEmail author
  • Gianpaolo Francesco Trotta
  • Peter Mohr-Ziak
  • Christina Gsaxner
  • Jürgen Wallner
  • Vitoantonio Bevilacqua
  • Jan Egger
Original Paper
  • 58 Downloads

Abstract

As of common routine in tumor resections, surgeons rely on local examinations of the removed tissues and on the swiftly made microscopy findings of the pathologist, which are based on intraoperatively taken tissue probes. This approach may imply an extended duration of the operation, increased effort for the medical staff, and longer occupancy of the operating room (OR). Mixed reality technologies, and particularly augmented reality, have already been applied in surgical scenarios with positive initial outcomes. Nonetheless, these methods have used manual or marker-based registration. In this work, we design an application for a marker-less registration of PET-CT information for a patient. The algorithm combines facial landmarks extracted from an RGB video stream, and the so-called Spatial-Mapping API provided by the HMD Microsoft HoloLens. The accuracy of the system is compared with a marker-based approach, and the opinions of field specialists have been collected during a demonstration. A survey based on the standard ISO-9241/110 has been designed for this purpose. The measurements show an average positioning error along the three axes of (x, y, z) = (3.3 ± 2.3, − 4.5 ± 2.9, − 9.3 ± 6.1) mm. Compared with the marker-based approach, this shows an increment of the positioning error of approx. 3 mm along two dimensions (x, y), which might be due to the absence of explicit markers. The application has been positively evaluated by the specialists; they have shown interest in continued further work and contributed to the development process with constructive criticism.

Keywords

Computer Assisted Surgery Microsoft HoloLens Pattern Recognition Head and Neck Cancer Augmented Reality Visualization Facial Landmarks Registration 

Notes

Acknowledgments

The authors would like to thank the team Kalkofen at the Institute of Computer Graphics and Vision, TU Graz, for their support.

Funding Information

This work received funding from the Austrian Science Fund (FWF) KLI 678-B31: “enFaced: Virtual and Augmented Reality Training and Navigation Module for 3D-Printed Facial Defect Reconstructions” (Principal Investigators: Drs. Jürgen Wallner and Jan Egger), the TU Graz Lead Project (Mechanics, Modeling and Simulation of Aortic Dissection), and CAMed (COMET K-Project 871132), which is funded by the Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT) and the Austrian Federal Ministry for Digital and Economic Affairs (BMDW) and the Styrian Business Promotion Agency (SFG).

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Copyright information

© Society for Imaging Informatics in Medicine 2019

Authors and Affiliations

  1. 1.Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical EngineeringGraz University of TechnologyGrazAustria
  2. 2.Computer Algorithms for Medicine LaboratoryGrazAustria
  3. 3.Department of Mechanics, Mathematics and ManagementPolytechnic University of BariBariItaly
  4. 4.VRVis-Zentrum für Virtual Reality und Visualisierung Forschungs-GmbHViennaAustria
  5. 5.Department of Oral & Maxillofacial SurgeryMedical University of GrazGrazAustria
  6. 6.Department of Electrical and Information EngineeringPolytechnic University of BariBariItaly

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