CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor何晖光
Degree Grantor中国科学院研究生院
Place of Conferral北京
Keyword2d/3d配准 学习 几何 统计形状模型 实时
本方法的两个姿态参数的平均预测误差为 和 ,平均目标配准误差mTRE(mean Target Registration Error)为0.87mm,平均配准时间为0.9s。实验结果表明本方法具有很好的实时性和准确性。
Other AbstractIn vertebra operation, an effective 2D/3D registration is of great importance and a challenging task. Traditional 2D/3D registration projects 3D data to 2D plan and conducts a 2D-2D registration. It is difficult to obtain both high accuracy and high real-time performance, because it has  projection space complexity composed of 3 translation and 3 rotation degrees of freedom.
In this thesis we proposed a method which combined learning strategy and geometric transformation. We built a shape model using statistical shape model and constructed a new projection method, which made it possible that 4 of 6 projection parameters can be calculated by geometric method. Thus, the parameters we need calculate using learning strategy reduced to 2 from 6, which significantly improved efficency of projection and calculation. Then we used regression learning method to learn a pose model between shape of vertebra and projection parameters, which avoided the compute intensive searching step and improved efficiency of calculation. Finally we fulfilled the registration by using the pose model we learned and geometric transformation.
Using the proposed method, the mean predict error of pose parameters is  and . The mean Target Registration Error (mTRE) is 0.87mm. And the mean time of registration is 0.9s. The result shows our proposed method owns both high accuracy and high real-time performance.
Building the model of vertebra and signing markers, which is of great importance, is needed in this work. To reduce the workload and build the model better, this work developed a tool to help sign markers, which improved the efficiency greatly.
Subject Area医学图像处理与分析
Document Type学位论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
陈智强. 基于学习和几何变换的2D/3D配准及在椎弓根手术中的应用[D]. 北京. 中国科学院研究生院,2017.
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