Deformable Registration Framework for Augmented Reality-based Surgical Guidance in Head and Neck Tumor Resection

2025-03-11

AI Summary

Following head and neck tumor resection, significant specimen shrinkage and anatomical complexity lead to substantial positive-margin relocalization errors using frozen section analysis (FSA)—clinically averaging 9.8 cm—increasing recurrence risk. To address this, we propose a thickness-aware deformable registration framework that fuses preoperative surface anatomy with post-resection defect geometry. This work introduces specimen thickness as a novel driving cue for registration—first of its kind—and employs tissue-specific 3D deformation modeling strategies tailored to tongue, buccal mucosa, and skin. Furthermore, we develop an end-to-end augmented reality (AR) visualization pipeline enabling real-time, dynamically aligned overlay of FSA results onto the surgical field. Validation on tongue specimens reduces target registration error (TRE) by 33%. In a clinical pilot study, surgeon-performed margin relocalization error decreased from 9.8 cm to 4.8 cm, demonstrating substantial improvement in intraoperative spatial accuracy and potential impact on oncologic outcomes.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Improves margin localization in head and neck tumor resection.
• Addresses specimen shrinkage and 3D anatomy complexity.
• Enhances intraoperative visualization with augmented reality.

Innovation

• Deformable registration framework for surgical guidance
• Incorporates thickness information using pre- and post-resection data
• Augmented reality system for accurate margin overlay