Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

All-in-One Augmented Reality Guided Head and Neck Tumor Resection

Yue Yang, Matthieu Chabanas, Carrie Reale, Annie Benson, Jason Slagle, Matthew Weinger, Michael Topf, Jie Ying Wu

Abstract

Positive margins are common in head and neck squamous cell carcinoma, yet intraoperative re-resection is often imprecise because margin locations are typically communicated verbally from pathology. We present an all-in-one augmented reality (AR) system that relocalizes positive margins from a resected specimen to the resection bed and visualizes them in situ using HoloLens 2 depth sensing and fully automated markerless surface registration. In a silicone phantom study with six medical trainees, markerless registration achieved target registration errors comparable to a marker-based baseline (median 1.8 mm vs. 1.7 mm; maximum < 4 mm). In a margin relocalization task, AR guidance reduced error from verbal guidance (median 14.2 mm) to a few millimeters (median 3.2 mm), with all AR localizations within 5 mm error. These results support the feasibility of markerless AR margin guidance for more precise intraoperative re-excision.

All-in-One Augmented Reality Guided Head and Neck Tumor Resection

Abstract

Positive margins are common in head and neck squamous cell carcinoma, yet intraoperative re-resection is often imprecise because margin locations are typically communicated verbally from pathology. We present an all-in-one augmented reality (AR) system that relocalizes positive margins from a resected specimen to the resection bed and visualizes them in situ using HoloLens 2 depth sensing and fully automated markerless surface registration. In a silicone phantom study with six medical trainees, markerless registration achieved target registration errors comparable to a marker-based baseline (median 1.8 mm vs. 1.7 mm; maximum < 4 mm). In a margin relocalization task, AR guidance reduced error from verbal guidance (median 14.2 mm) to a few millimeters (median 3.2 mm), with all AR localizations within 5 mm error. These results support the feasibility of markerless AR margin guidance for more precise intraoperative re-excision.

Paper Structure

This paper contains 13 sections, 9 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Resection-bed and specimen surface acquisition
  4. Fully automated markerless specimen-to-bed registration
  5. Coarse global registration
  6. Automatic ROI construction
  7. Fine registration
  8. Results
  9. Registration accuracy
  10. Margin relocalization task performance
  11. Workload: NASA-TLX & System Usability Scale (SUS)
  12. AR task-specific questionnaire
  13. Discussion and Conclusion

Figures (5)

  • Figure 1: System pipeline and coordinates: (a) AR-guided vs. traditional margin relocalization workflow. (b) System formulation.
  • Figure 2: User study setup: (A) Mixed-reality capture, (B) HMD, (C) tracked stylus, (D) phantom (marker + markerless), (E) close-up mic/camera.
  • Figure 3: (a) TRE for marker vs. markerless registration. (b) Margin relocalization error for verbal vs. AR guidance.
  • Figure 4: (a) NASA-TLX for margin relocalization. (b) System Usability Scale.
  • Figure 5: AR task-specific ratings