Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching.
Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching.
复制标题
基于鲁棒主动形状模型匹配的 4D CT 体积中的肺部分割。
DOI：
10.1155/2015/125648
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发表时间：
2015
影响因子：
7.6
通讯作者：
Beichel,ReinhardR
中科院分区：
文献类型：
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作者：
Gill,Gurman;Beichel,ReinhardR
动态和纵向肺部CT成像可生成4D肺部图像数据集，从而实现放射治疗计划或评估肺部疾病治疗反应等应用。在本文中，我们提出了一个4D肺分割方法，相互利用所有个人的CT体积，以获得每个CT数据集的分割。我们的方法是基于一个3D强大的主动形状模型，并将其扩展到充分利用4D肺部图像数据集。这产生了4D体积的初始分割，然后通过使用4D最佳表面查找算法对其进行细化。该方法在152个正常和病变肺的CT扫描的不同集合上进行了评估，包括总肺容量和功能性残气量扫描对。此外，还对3D分割方法和基于配准的4D肺分割方法进行了比较。提出的4D方法获得的平均Dice系数为0.9773 ± 0.0254，在统计学上显著优于（p值<0.001）3D方法（0.9659 ± 0.0517）。与基于配准的4D方法相比，我们的方法获得了更好或相似的性能，但速度提高了58.6%。此外，该方法可以很容易地扩展到处理由几个体积组成的4D CT数据集。
Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. Our approach is based on a 3D robust active shape model and extends it to fully utilize 4D lung image data sets. This yields an initial segmentation for the 4D volume, which is then refined by using a 4D optimal surface finding algorithm. The approach was evaluated on a diverse set of 152 CT scans of normal and diseased lungs, consisting of total lung capacity and functional residual capacity scan pairs. In addition, a comparison to a 3D segmentation method and a registration based 4D lung segmentation approach was performed. The proposed 4D method obtained an average Dice coefficient of 0.9773 ± 0.0254, which was statistically significantly better (pvalue ≪0.001) than the 3D method (0.9659 ± 0.0517). Compared to the registration based 4D method, our method obtained better or similar performance, but was 58.6% faster. Also, the method can be easily expanded to process 4D CT data sets consisting of several volumes.