用深度学习让脑组织变形预测更真实，误差减半且速度不降。
Deep Biomechanically-Guided Interpolation for Keypoint-Based Brain Shift Registration
- 基于生物力学模拟生成数据，训练网络从稀疏点推导密集变形场。
- 在仿真数据上将均方误差降低50%，推理开销几乎为零。
- 适合神经外科导航中需要高精度形变估计的场景。
术中脑移位准确补偿对维持神经导航可靠性至关重要。虽然基于关键点的配准方法对大变形和拓扑变化具有鲁棒性，但通常依赖忽略组织生物力学的简单几何插值来生成密集位移场。本文提出一种新型深度学习框架，从稀疏匹配的关键点估计出密集且符合物理规律的脑组织变形。首先利用生物力学仿真生成大规模合成脑变形数据集；随后训练一个残差3D U-Net，将标准插值结果优化为生物力学引导的变形。在大量仿真位移场上的实验表明，该方法显著优于经典插值器，在将均方误差减半的同时，推理时引入的计算开销可忽略不计。代码已开源：https://github.com/tiago-assis/Deep-Biomechanical-Interpolator。
原文摘要 · Abstract (English)
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}.
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