ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2026
Augmented Reality-based Guidance with Deformable Registration in Head and Neck Tumor Resection.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence in Head and Neck Surgical Oncology: A State-of-the-Art Review.Journal of clinical medicine · 2026Review
- Toward a Surgical Clip-Based Registration Approach for Aligning Pathology Data to Tumor Bed Using Mixed Reality.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Head and neck squamous cell carcinoma has one of the highest rates of recurrence. Recurrence rates can be reduced by accurate localization of positive margins. While frozen section analysis of resected specimens provides accurate intraoperative margin assessment, complex 3D anatomy and significant shrinkage of resected specimens complicate margin relocation from the specimen back to the post-resection cavity. We propose a novel deformable registration framework that uses both the pre-resection external surface and the post-resection cavity of the specimen to incorporate thickness information. In tongue specimens, the proposed framework improved the target registration error (TRE) by up to 33% as compared to using the post-resection cavity alone. We found distinct deformation behaviors in skin, buccal, and tongue specimens, highlighting the need for tailored deformation strategies. Notably, tongue specimens hold the highest clinical need for improvement among head and neck specimens. To further aid intraoperative visualization, we also integrated this framework into an augmented reality-based guidance system. This system can automatically overlay the deformed 3D specimen mesh with positive margin annotation onto the post-resection cavity. The integrated system improved a surgeon and a trainee's average relocation error from 9.8 mm to 4.8 mm in a pilot study. Our implementation code for AR guidance and generating the target point cloud is available at https://github.com/vu-maple-lab/Head-and-Neck-Tumor-Resection-Guidance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.