A Spatial Registration Method Based on Point Cloud and Deep Learning for Augmented Reality Neurosurgical Navigation

IF 2.1 3区医学 Q2 SURGERY International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2024-12-23 DOI:10.1002/rcs.70030

Zifeng Liu, Zhiyong Yang, Shan Jiang, Zeyang Zhou

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Abstract

Background

In order to achieve spatial registration for surgical navigation, a spatial registration method based on point cloud and deep learning is proposed.

Methods

Neural networks are used to register medical image point clouds and patient surface point clouds to complete spatial registration of surgical navigation. An image processing method is designed to convert medical images into point clouds, and a structured light robot is used to extract patient surface point clouds.

Results

Coarse registration was conducted through a neural network, followed by fine registration using the ICP algorithm, achieving a rotational registration error (RRE) of 0.961° and a translational registration error (TRE) of 0.118 mm. In phantom experiments, the surface registration error was 0.622 mm, and the target registration error was 0.748 mm.

Conclusions

The proposed spatial registration method based on point cloud and deep learning improves the accuracy and efficiency of neurosurgical navigation.

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基于点云和深度学习的增强现实神经外科导航空间配准方法。

背景：为了实现手术导航的空间配准，提出了一种基于点云和深度学习的空间配准方法。方法：利用神经网络对医学图像点云和患者体表点云进行配准，完成手术导航的空间配准。设计了一种将医学图像转化为点云的图像处理方法，并利用结构光机器人提取患者表面点云。结果：采用神经网络进行粗配准，ICP算法进行精细配准，旋转配准误差（RRE）为0.961°，平移配准误差（TRE）为0.118 mm。幻影实验中，表面配准误差为0.622 mm，目标配准误差为0.748 mm。结论：提出的基于点云和深度学习的空间配准方法提高了神经外科导航的精度和效率。

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来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

CiteScore

4.50

自引率

12.00%

发文量

131

审稿时长

6-12 weeks

期刊介绍： The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.