Zhou Haoyin, Jagadeesan Jayender
Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Med Image Comput Comput Assist Interv. 2019 Oct;11764:339-347. doi: 10.1007/978-3-030-32239-7_38. Epub 2019 Oct 10.
Tissue deformation during the surgery may significantly decrease the accuracy of surgical navigation systems. In this paper, we propose an approach to estimate the deformation of tissue surface from stereo videos in real-time, which is capable of handling occlusion, smooth surface and fast deformation. We first use a stereo matching method to extract depth information from stereo video frames and generate the tissue template, and then estimate the deformation of the obtained template by minimizing ICP, ORB feature matching and as-rigid-as-possible (ARAP) costs. The main novelties are twofold: (1) Due to non-rigid deformation, feature matching outliers are difficult to be removed by traditional RANSAC methods; therefore we propose a novel 1-point RANSAC and reweighting method to preselect matching inliers, which handles smooth surfaces and fast deformations. (2) We propose a novel ARAP cost function based on dense connections between the control points to achieve better smoothing performance with limited number of iterations. Algorithms are designed and implemented for GPU parallel computing. Experiments on and data showed that this approach works at an update rate of 15 Hz with an accuracy of less than 2.5 mm on a NVIDIA Titan X GPU.
手术过程中的组织变形可能会显著降低手术导航系统的准确性。在本文中，我们提出了一种从立体视频实时估计组织表面变形的方法，该方法能够处理遮挡、光滑表面和快速变形。我们首先使用立体匹配方法从立体视频帧中提取深度信息并生成组织模板，然后通过最小化ICP、ORB特征匹配和尽可能刚体（ARAP）代价来估计所得模板的变形。主要创新点有两个方面：（1）由于非刚性变形，传统的RANSAC方法难以去除特征匹配的异常值；因此，我们提出了一种新颖的1点RANSAC和重新加权方法来预选匹配内点，该方法可以处理光滑表面和快速变形。（2）我们提出了一种基于控制点之间密集连接的新颖ARAP代价函数，以在有限的迭代次数下实现更好的平滑性能。算法针对GPU并行计算进行了设计和实现。在[具体数据]和[具体数据]上的实验表明，该方法在NVIDIA Titan X GPU上以15 Hz的更新速率运行，精度小于2.5 mm。
