深度生物力学引导的关键点脑移位注册
Deep Biomechanically-Guided Interpolation for Keypoint-Based Brain Shift Registration
AI总结：
本文提出了一种深度学习框架，利用生物力学模拟生成合成数据，并通过残差3D U-Net实现关键点驱动的脑移位注册，有效提升形变估计精度。
摘要：
准确补偿脑移位对于维持神经导航在神经外科手术中的可靠性至关重要。虽然基于关键点的注册方法对大形变和拓扑变化具有鲁棒性，但它们通常依赖于简单的几何插值器，忽略了组织生物力学来创建密集的位移场。在本工作中，我们提出了一种新的深度学习框架，该框架从稀疏匹配的关键点估计出密集且物理上合理的脑形变。我们首先使用生物力学模拟生成一个大规模的合成脑形变数据集。然后，一个残差3D U-Net被训练用来将标准插值估计细化为生物力学引导的形变。在大量模拟位移场上的实验表明，我们的方法显著优于经典插值器，将均方误差减少了一半，同时在推理时间引入了可忽略的计算开销。代码可在：https://github.com/tiago-assis/Deep-Biomechanical-Interpolator获得。
英文摘要：
Accurate compensation of brain shift is critical for maintaining the reliability of neuronavigation during neurosurgery. While keypoint-based registration methods offer robustness to large deformations and topological changes, they typically rely on simple geometric interpolators that ignore tissue biomechanics to create dense displacement fields. In this work, we propose a novel deep learning framework that estimates dense, physically plausible brain deformations from sparse matched keypoints. We first generate a large dataset of synthetic brain deformations using biomechanical simulations. Then, a residual 3D U-Net is trained to refine standard interpolation estimates into biomechanically guided deformations. Experiments on a large set of simulated displacement fields demonstrate that our method significantly outperforms classical interpolators, reducing by half the mean square error while introducing negligible computational overhead at inference time. Code available at: \href{https://github.com/tiago-assis/Deep-Biomechanical-Interpolator}{https://github.com/tiago-assis/Deep-Biomechanical-Interpolator}.
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。
