Abstract：
In vitro fenestration of stent-graft (IVFS) demands high-precision navigation methods to achieve optimal surgical outcomes. This study aims to propose an augmented reality (AR) navigation method for IVFS, which can provide in situ overlay display to locate fenestration positions.
We propose an AR navigation method to assist doctors in performing IVFS. A deep learning-based aorta segmentation algorithm is used to achieve automatic and rapid aorta segmentation. The Vuforia-based virtual-real registration and marker recognition algorithm are integrated to ensure accurate in situ AR image.
The proposed method can provide three-dimensional in situ AR image, and the fiducial registration error after virtual-real registration is 2.070 mm. The aorta segmentation experiment obtains dice similarity coefficient of 91.12% and Hausdorff distance of 2.59, better than conventional algorithms before improvement.
The proposed method can intuitively and accurately locate fenestration positions, and therefore can assist doctors in performing IVFS.
We propose an AR navigation method to assist doctors in performing IVFS. A deep learning-based aorta segmentation algorithm is used to achieve automatic and rapid aorta segmentation. The Vuforia-based virtual-real registration and marker recognition algorithm are integrated to ensure accurate in situ AR image.
The proposed method can provide three-dimensional in situ AR image, and the fiducial registration error after virtual-real registration is 2.070 mm. The aorta segmentation experiment obtains dice similarity coefficient of 91.12% and Hausdorff distance of 2.59, better than conventional algorithms before improvement.
The proposed method can intuitively and accurately locate fenestration positions, and therefore can assist doctors in performing IVFS.
摘要：
■支架移植物（IVFS）的体外开窗术需要高精度的导航方法来达到最佳的手术效果。本研究旨在提出一种用于IVFS的增强现实(AR)导航方法，它可以提供原位叠加显示来定位开窗位置。
■我们提出了一种AR导航方法，以协助医生进行IVFS。采用基于深度学习的主动脉分割算法实现了主动脉的自动快速分割。集成了基于Vuforia的虚实配准和标记识别算法，以确保准确的原位AR图像。
■所提出的方法可以提供三维原位AR图像，虚实配准后的基准配准误差为2.070mm。主动脉分割实验获得的骰子相似系数为91.12%，Hausdorff距离为2.59，优于改进前的常规算法。
■所提出的方法可以直观，准确地定位开窗位置，因此可以协助医生进行IVFS。
■我们提出了一种AR导航方法，以协助医生进行IVFS。采用基于深度学习的主动脉分割算法实现了主动脉的自动快速分割。集成了基于Vuforia的虚实配准和标记识别算法，以确保准确的原位AR图像。
■所提出的方法可以提供三维原位AR图像，虚实配准后的基准配准误差为2.070mm。主动脉分割实验获得的骰子相似系数为91.12%，Hausdorff距离为2.59，优于改进前的常规算法。
■所提出的方法可以直观，准确地定位开窗位置，因此可以协助医生进行IVFS。
