Zheng Guoyan, Gerber Nicolas, Widmer Daniel, Stieger Christof, Caversaccio Marco, Nolte Lutz-Peter, Weber Stefan
Institute for Surgical Technology and Biomechanics, University of Bern, Switzerland.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2325-8. doi: 10.1109/IEMBS.2010.5627459.
This paper presents an automated solution for precise detection of fiducial screws from three-dimensional (3D) Computerized Tomography (CT)/Digital Volume Tomography (DVT) data for image-guided ENT surgery. Unlike previously published solutions, we regard the detection of the fiducial screws from the CT/DVT volume data as a pose estimation problem. We thus developed a model-based solution. Starting from a user-supplied initialization, our solution detects the fiducial screws by iteratively matching a computer aided design (CAD) model of the fiducial screw to features extracted from the CT/DVT data. We validated our solution on one conventional CT dataset and on five DVT volume datasets, resulting in a total detection of 24 fiducial screws. Our experimental results indicate that the proposed solution achieves much higher reproducibility and precision than the manual detection. Further comparison shows that the proposed solution produces better results on the DVT dataset than on the conventional CT dataset.
本文提出了一种自动化解决方案，用于从三维（3D）计算机断层扫描（CT）/数字容积断层扫描（DVT）数据中精确检测基准螺钉，以用于图像引导的耳鼻喉科手术。与先前发表的解决方案不同，我们将从CT/DVT容积数据中检测基准螺钉视为一个位姿估计问题。因此，我们开发了一种基于模型的解决方案。从用户提供的初始化开始，我们的解决方案通过将基准螺钉的计算机辅助设计（CAD）模型与从CT/DVT数据中提取的特征进行迭代匹配来检测基准螺钉。我们在一个传统CT数据集和五个DVT容积数据集上对我们的解决方案进行了验证，共检测到24个基准螺钉。我们的实验结果表明，所提出的解决方案比手动检测具有更高的可重复性和精度。进一步比较表明，所提出的解决方案在DVT数据集上的效果优于传统CT数据集。
