# Augmented Reality-based Guidance with Deformable Registration in Head and Neck Tumor Resection

arXiv: https://arxiv.org/abs/2503.08802 · HTML: https://mlsanity.com/paper/2503.08802/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 2 of 2)

- [LIBR+: Improving Intraoperative Liver Registration by Learning the Residual of Biomechanics-Based Deformable Registration](https://mlsanity.com/paper/2403.06901/) (2024)
- [Leveraging Vision and Kinematics Data to Improve Realism of Biomechanic Soft-tissue Simulation for Robotic Surgery](https://mlsanity.com/paper/2003.06518/) (2020)

## Built on by (most cited first, 2 of 2)

- [Multi-Camera AR Guidance System for Surgical Instrument Handling and Assembly: Investigating Workload and Efficiency](https://mlsanity.com/paper/2606.04992/) (2026)
- [Marker-free deformable registration and fusion for augmented reality-guided positive margin localization during tumor resection surgery](https://mlsanity.com/paper/2607.13343/) (2026)

## Abstract (arXiv)

Head and neck squamous cell carcinoma (HNSCC) has one of the highest rates of recurrence cases among solid malignancies. Recurrence rates can be reduced by improving positive margins localization. Frozen section analysis (FSA) of resected specimens is the gold standard for intraoperative margin assessment. However, because of the complex 3D anatomy and the significant shrinkage of resected specimens, accurate margin relocation from specimen back onto the resection site based on FSA results remains challenging. We propose a novel deformable registration framework that uses both the pre-resection upper surface and the post-resection site of the specimen to incorporate thickness information into the registration process. The proposed method significantly improves target registration error (TRE), demonstrating enhanced adaptability to thicker specimens. In tongue specimens, the proposed framework improved TRE by up to 33% as compared to prior deformable registration. Notably, tongue specimens exhibit complex 3D anatomies and hold the highest clinical significance compared to other head and neck specimens from the buccal and skin. We analyzed distinct deformation behaviors in different specimens, highlighting the need for tailored deformation strategies. To further aid intraoperative visualization, we also integrated this framework with an augmented reality-based auto-alignment system. The combined system can accurately and automatically overlay the deformed 3D specimen mesh with positive margin annotation onto the resection site. With a pilot study of the AR guided framework involving two surgeons, the integrated system improved the surgeons' average target relocation error from 9.8 cm to 4.8 cm.