Rapid surface registration of 3D volumes using a neural network approach
Rapid surface registration of 3D volumes using a neural network approach
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DOI：
10.1016/j.imavis.2007.04.003
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发表时间：
2008-02-01
影响因子：
4.7
通讯作者：
Yan, C. H.
中科院分区：
文献类型：
--
作者：
Zhang, J.;Ge, Y.;Yan, C. H.
提出了一种基于神经网络的自动曲面刚性配准系统。该系统已应用于图像引导手术中的人体骨结构配准。多层感知器神经网络用于从术前图像构建患者特异性表面模型。然后，从所得神经网络模型导出的表面表示函数用于术中配准。通过优化过程获得最佳变换参数。这种分割/配准系统实现了与传统技术相当的亚体素精度，并且速度明显更快。这些优点使用跟骨和椎骨的图像数据集来证明。(c)2007 Elsevier B. V.保留所有权利。
An automatic surface-based rigid registration system using a neural network representation is proposed. The system has been applied to register human bone structures for image-guided surgery. A multilayer perceptron neural network is used to construct a patient-specific surface model from pre-operative images. A surface representation function derived from the resultant neural network model is then employed for intra-operative registration. The optimal transformation parameters are obtained via an optimization process. This segmentation/registration system achieves sub-voxel accuracy comparable to that of conventional techniques, and is significantly faster. These advantages are demonstrated using image datasets of the calcaneus and vertebrae. (c) 2007 Elsevier B.V. All rights reserved.