高精度重建柔性导管等任意形状长条体，适合医疗手术模拟
ACCURATE: Arbitrary-shaped Continuum Reconstruction Under Robust Adaptive Two-view Estimation
- 结合图像分割与几何约束的动态规划算法
- 真实和仿真数据均达1.0mm以内平均误差
- 抗遮挡、抗噪声，适用于临床X光系统
准确重建如导丝、导管等任意形状长细柔性体，对精确机械仿真至关重要。现有基于图像的重建方法常因未充分利用相机几何信息，或依赖刚性几何假设而泛化性差，难以应对高度可变形的连续体机器人。为此，我们提出ACCURATE框架，融合图像分割神经网络与几何约束拓扑遍历及动态规划算法，强制实现全局双平面几何一致性，最小化累积点到极线距离，并对遮挡及由噪声和离散化引起的极线模糊具有鲁棒性。该方法在使用临床X光C臂系统获取的真实与仿真幻影数据集上均实现低于1.0 mm的平均绝对误差。
原文摘要 · Abstract (English)
Accurate reconstruction of arbitrary-shaped long slender continuum bodies, such as guidewires, catheters and other soft continuum manipulators, is essential for accurate mechanical simulation. However, existing image-based reconstruction approaches often suffer from limited accuracy because they often underutilize camera geometry, or lack generality as they rely on rigid geometric assumptions that may fail for continuum robots with complex and highly deformable shapes. To address these limitations, we propose ACCURATE, a 3D reconstruction framework integrating an image segmentation neural network with a geometry-constrained topology traversal and dynamic programming algorithm that enforces global biplanar geometric consistency, minimizes the cumulative point-to-epipolar-line distance, and remains robust to occlusions and epipolar ambiguities cases caused by noise and discretization. Our method achieves high reconstruction accuracy on both simulated and real phantom datasets acquired using a clinical X-ray C-arm system, with mean absolute errors below 1.0 mm.
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