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用于机器人辅助微创手术的腹腔镜自校准

Laparoscope self-calibration for robotic assisted minimally invasive surgery.

作者信息

Stoyanov Danail, Darzi Ara, Yang Guang-Zhong

机构信息

Royal Society/Wolfson Foundation Medical Image Computing Laboratory, Imperial College of Science, Technology and Medicine, London SW7 2BZ, UK.

出版信息

Med Image Comput Comput Assist Interv. 2005;8(Pt 2):114-21. doi: 10.1007/11566489_15.

Abstract

For robotic assisted minimal access surgery, recovering 3D soft tissue deformation is important for intra-operative surgical guidance, motion compensation, and prescribing active constraints. We propose in this paper a method for determining varying focal lengths of stereo laparoscope cameras during robotic surgery. Laparoscopic images typically feature dynamic scenes of soft-tissue deformation and self-calibration is difficult with existing approaches due to the lack of rigid temporal constraints. The proposed method is based on the direct derivation of the focal lengths from the fundamental matrix of the stereo cameras with known extrinsic parameters. This solves a restricted self-calibration problem, and the introduction of the additional constraints improves the inherent accuracy of the algorithm. The practical value of the method is demonstrated with analysis of results from both synthetic and in vivo data sets.

摘要

对于机器人辅助微创外科手术而言,恢复三维软组织变形对于术中手术引导、运动补偿以及施加主动约束非常重要。本文提出了一种在机器人手术过程中确定立体腹腔镜相机可变焦距的方法。腹腔镜图像通常具有软组织变形的动态场景,并且由于缺乏刚性时间约束,现有方法难以进行自校准。所提出的方法基于从具有已知外部参数的立体相机的基本矩阵直接推导焦距。这解决了一个受限的自校准问题,并且引入额外约束提高了算法的固有精度。通过对合成数据集和体内数据集的结果分析,证明了该方法的实用价值。

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