首页> 外国专利> Guidance method based on 3D-2D pose estimation and 3D-CT registration with application to live bronchoscopy

Guidance method based on 3D-2D pose estimation and 3D-CT registration with application to live bronchoscopy

机译:基于3D-2D姿态估计和3D-CT配准的制导方法及其在现场支气管镜中的应用

摘要

A method provides guidance to the physician during a live bronchoscopy or other endoscopic procedures. The 3D motion of the bronchoscope is estimated using a fast coarse tracking step followed by a fine registration step. The tracking is based on finding a set of corresponding feature points across a plurality of consecutive bronchoscopic video frames, then estimating for the new pose of the bronchoscope. In the preferred embodiment the pose estimation is based on linearization of the rotation matrix. By giving a set of corresponding points across the current bronchoscopic video image, and the CT-based virtual image as an input, the same method can also be used for manual registration. The fine registration step is preferably a gradient-based Gauss-Newton method that maximizes the correlation between the bronchoscopic video image and the CT-based virtual image. The continuous guidance is provided by estimating the 3D motion of the bronchoscope in a loop. Since depth-map information is available, tracking can be done by solving a 3D-2D pose estimation problem. A 3D-2D pose estimation problem is more constrained than a 2D-2D pose estimation problem and does not suffer from the limitations associated with computing an essential matrix. The use of correlation-based cost, instead of mutual information as a registration cost, makes it simpler to use gradient-based methods for registration.
机译:一种方法在活支气管镜检查或其他内窥镜检查过程中为医师提供指导。支气管镜的3D运动是使用快速粗略跟踪步骤和精细配准步骤估算的。跟踪基于在多个连续的支气管镜视频帧上找到一组相应的特征点,然后估计支气管镜的新姿势。在优选实施例中,姿态估计基于旋转矩阵的线性化。通过在当前的支气管镜视频图像上提供一组对应点,并将基于CT的虚拟图像作为输入,相同的方法也可以用于手动配准。精细配准步骤优选地是基于梯度的高斯-牛顿法,其使支气管镜视频图像与基于CT的虚拟图像之间的相关性最大化。通过估算循环中支气管镜的3D运动来提供连续引导。由于深度图信息可用,因此可以通过解决3D-2D姿态估计问题来完成跟踪。 3D-2D姿势估计问题比2D-2D姿势估计问题更受约束,并且不会遭受与计算基本矩阵相关的限制。使用基于相关的成本而不是互信息作为注册成本,可以更轻松地使用基于梯度的方法进行注册。

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