A new approach for tubular structure modeling and segmentation using graph-based techniques.
A new approach for tubular structure modeling and segmentation using graph-based techniques.
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使用基于图形的技术进行管状结构建模和分割的新方法。
DOI：
10.1007/978-3-642-23626-6_38
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Dawant,BenoitM
中科院分区：
文献类型：
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作者：
Noble,JackH;Dawant,BenoitM
在这项工作中，管状结构分割的新方法。该方法由两部分组成：（1）从手动分割的样本自动构建模型和（2）使用这些模型分割未知图像中的结构。分割问题通过在高维图中寻找最优路径来解决。该图设计了新的结构，允许将先验信息从模型到优化过程中，并占传统的基于图的方法的几个弱点。该方法的通用性证明了四个具有挑战性的分割任务：视神经通路，面神经，鼓索，颈动脉测试。在所有四种情况下，实现了自动和手动分割之间的良好一致性。
In this work, a new approach for tubular structure segmentation is presented. This approach consists of two parts: (1) automatic model construction from manually segmented exemplars and (2) segmentation of structures in unknown images using these models. The segmentation problem is solved by finding an optimal path in a high-dimensional graph. The graph is designed with novel structures that permit the incorporation of prior information from the model into the optimization process and account for several weaknesses of traditional graph-based approaches. The generality of the approach is demonstrated by testing it on four challenging segmentation tasks: the optic pathways, the facial nerve, the chorda tympani, and the carotid artery. In all four cases, excellent agreement between automatic and manual segmentations is achieved.