Joint registration and segmentation of serial lung CT images for image-guided lung cancer diagnosis and therapy.
Joint registration and segmentation of serial lung CT images for image-guided lung cancer diagnosis and therapy.
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DOI：
10.1016/j.compmedimag.2009.05.007
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发表时间：
2010-01
影响因子：
5.7
通讯作者：
Wong, Stephen T. C.
中科院分区：
文献类型：
--
作者：
Xue, Zhong;Wong, Kelvin;Wong, Stephen T. C.
在肺周围小病变的图像引导诊断和治疗中，术前肺部CT图像和术中图像的对准是准确引导和监测介入手术的重要步骤。配准序列图像通常依赖于图像的正确分割，另一方面，可以通过序列图像的时间对准来进一步改善分割结果。本文提出了一种联合序列图像配准和分割算法。在该算法中，序列图像分割的基础上，当前的变形，和序列图像之间的变形更新的分割结果的基础上迭代细化。变形场的时间平滑度不被强制执行，使得算法可以容忍在图像引导治疗期间经常出现的较大或不连续的时间变化。物理过程模型也可以纳入我们的框架，以更好地处理干预期间的序列图像的时间变化。在实验中，我们应用所提出的算法来对齐序列肺部CT图像。仿真图像和临床图像的实验结果表明，该算法比仅使用变形配准的方法具有更好的鲁棒性。
In image-guided diagnosis and treatment of small peripheral lung lesions the alignment of the pre-procedural lung CT images and the intra-procedural images is an important step to accurately guide and monitor the interventional procedure. Registering the serial images often relies on correct segmentation of the images and, on the other hand, the segmentation results can be further improved by temporal alignment of the serial images. This paper presents a joint serial image registration and segmentation algorithm. In this algorithm, serial images are segmented based on the current deformations, and the deformations among the serial images are iteratively refined based on the updated segmentation results. No temporal smoothness about the deformation fields is enforced so that the algorithm can tolerate larger or discontinuous temporal changes that often appear during image-guided therapy. Physical procedure models could also be incorporated to our framework to better handle the temporal changes of the serial images during intervention. In experiments, we apply the proposed algorithm to align serial lung CT images. Results using both simulated and clinical images show that the new algorithm is more robust compared to the method that only uses deformable registration.
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DOI：
10.1109/tpami.2007.1120
发表时间：
2007-12-01
影响因子：
23.6
作者：
Droske, Marc;Rumpf, Martin
通讯作者：
Rumpf, Martin
影响因子：
5.7
作者：
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通讯作者：
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影响因子：
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Wang, Fei;Vemuri, Baba C.;Eisenschenk, Stephan J.
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影响因子：
5.7
作者：
Xue, Z;Shen, DG;Davatzikos, C
通讯作者：
Davatzikos, C
影响因子：
4.7
作者：
Marsland, Stephen;Twining, Carole J.;Taylor, Chris J.
通讯作者：
Taylor, Chris J.