{"slug": "meibrd-meta-learning-intraoperative-biomechanical-residual-deformation", "title": "MeiBRD: Meta-Learning Intraoperative Biomechanical Residual Deformation", "summary": "Researchers propose a hybrid framework for intraoperative liver registration that combines biomechanical priors with meta-learned residual corrections, achieving improved accuracy and generalization on deformable phantom data.", "body_md": "arXiv:2606.17379v1 Announce Type: new\nAbstract: Accurate intraoperative liver registration is challenging due to substantial soft-tissue deformation yet sparse intraoperative measurements. Biomechanical models regularize this ill-posedness with prior knowledge but exhibit persistent prediction bias due to simplifying assumptions, while data-driven learning solutions struggle with data efficiency, generalization, and physical plausibility. We propose a hybrid registration framework that adapts a biomechanical prior using sparse intraoperative correspondences. Rather than learning a full deformation field, we learn a residual deformation function that corrects linear biomechanical predictions, modeled as a graph neural diffusion function with geometry-aware attention over the 3D liver mesh. To enable long-range information transfer of sparse observations, we take a novel perspective of sparse intraoperative measurements as \\textit{context} samples where input-output pairs of the residual deformation function are fully observed, casting the problem into learning-to-learn this residual function from intraoperative context samples with feedforward meta-learners. Experiments on a deformable liver phantom dataset demonstrate improved registration accuracy and generalization compared to rigid, biomechanical, and data-driven baselines, particularly for out-of-distribution geometries and deformations.", "url": "https://wpnews.pro/news/meibrd-meta-learning-intraoperative-biomechanical-residual-deformation", "canonical_source": "https://arxiv.org/abs/2606.17379", "published_at": "2026-06-17 04:00:00+00:00", "updated_at": "2026-06-17 04:26:12.075651+00:00", "lang": "en", "topics": ["machine-learning", "computer-vision", "ai-research"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/meibrd-meta-learning-intraoperative-biomechanical-residual-deformation", "markdown": "https://wpnews.pro/news/meibrd-meta-learning-intraoperative-biomechanical-residual-deformation.md", "text": "https://wpnews.pro/news/meibrd-meta-learning-intraoperative-biomechanical-residual-deformation.txt", "jsonld": "https://wpnews.pro/news/meibrd-meta-learning-intraoperative-biomechanical-residual-deformation.jsonld"}}