Contour-Constrained Deformable Registration with Parameter Characterization for Head and Neck Surgical Guidance

A biomechanics-driven registration framework designed to correct post-resection tissue deformation during head and neck cancer surgery reduced target registration error by nearly half compared to rigid alignment, according to a preprint posted on arXiv [1]. Head and neck squamous cell carcinoma accounts for 890,000 new cases globally each year and carries one of the highest recurrence rates among solid malignancies [1][2]. Frozen section analysis remains the standard of care for intraoperative margin assessment, but accurately relocating positive margins on the resection bed is difficult because of imprecise alignment between the excised specimen and the surgical cavity, compounded by post-resection mucosal tissue shrinkage [1][2]. The new method, described in a paper submitted on 18 June 2026, registers three-dimensional specimen meshes to intraoperative resection bed point clouds using deformable registration based on regularized Kelvinlet basis functions [1][2]. It incorporates surface point clouds, fiducial landmarks, and boundary contour constraints that directly penalize perpendicular distance-to-agreement between specimen and resection bed boundaries [1][2]. Across nine specimens from skin, buccal mucosa, and tongue sites, rigid registration produced an overall mean target registration error of 11.11 ± 4.07 mm [1][2]. Deformable registration without the contour constraint reduced that error to 8.20 ± 2.68 mm, a 26.19% reduction [1][2]. Adding the contour constraint further lowered the mean error to 5.62 ± 2.28 mm, representing a 49.41% reduction relative to rigid registration [1][2]. The largest improvement was observed in tongue specimens, which the authors describe as the most clinically challenging [1][2]. A systematic two-stage parameter search showed that contour weighting dominates registration accuracy for tissue types exhibiting large lateral deformation, while the algorithm performs across a broad range of parameter combinations [1][2]. The work was led by Matthieu Chabanas and posted on arXiv, an open-access repository of electronic preprints that is moderated but not peer-reviewed [1][6]. As of November 2024, arXiv was receiving about 24,000 new articles per month and had surpassed two million total articles by the end of 2021 [6].