MeiBRD：元学习术中生物力学残余变形
MeiBRD: Meta-Learning Intraoperative Biomechanical Residual Deformation
AI总结：
提出混合配准框架，利用稀疏术中对应点自适应生物力学先验，通过图神经扩散函数学习残余变形，结合元学习从术中样本中快速适应，在肝脏体模上优于现有方法。
摘要：
由于软组织大幅变形且术中测量稀疏，精确的术中肝脏配准具有挑战性。生物力学模型通过先验知识正则化这一不适定问题，但由于简化假设而表现出持续的预测偏差，而数据驱动学习方法在数据效率、泛化能力和物理合理性方面存在困难。我们提出一个混合配准框架，利用稀疏术中对应点自适应生物力学先验。我们不是学习完整的变形场，而是学习一个校正线性生物力学预测的残余变形函数，该函数建模为图神经扩散函数，在3D肝脏网格上具有几何感知注意力。为了实现稀疏观测的长距离信息传递，我们从一个新颖的角度将稀疏术中测量视为\textit{上下文}样本，其中残余变形函数的输入-输出对完全观测，将问题转化为从术中上下文样本中学习该残余函数，使用前馈元学习器。在可变形肝脏体模数据集上的实验表明，与刚性、生物力学和数据驱动基线相比，配准精度和泛化能力得到提升，特别是在分布外几何和变形情况下。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
