Atlas-driven lung lobe segmentation in volumetric X-ray CT images
Atlas-driven lung lobe segmentation in volumetric X-ray CT images
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DOI：
10.1109/tmi.2005.859209
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发表时间：
2006-01-01
影响因子：
10.6
通讯作者：
Reinhardt, JM
中科院分区：
文献类型：
--
作者：
Zhang, L;Hoffman, EA;Reinhardt, JM
高分辨率X射线计算机断层扫描（CT）成像通常用于临床肺部应用。由于肺功能随区域而变化，并且由于肺部疾病通常在肺部分布不均匀，因此逐叶研究肺部是有用的。因此，重要的是不仅分割肺，而且分割叶裂。在本文中，我们演示了使用解剖肺图谱，编码与先验信息的肺解剖，自动分割斜叶裂。来自16名受试者的16次容积CT扫描用于构建肺图谱。对原始CT图像应用脊度测量以增强裂隙对比度。裂缝检测分两个阶段完成：初始裂缝搜索和最终裂缝搜索。一个模糊推理系统用于裂缝搜索分析信息从三个来源：图像强度，解剖平滑度约束，和基于图集的搜索初始化。我们的方法已在12名受试者的22次体积薄层CT扫描上进行了测试，并将结果与手动描记进行了比较。对所有22个数据集进行平均，自动分割和手动分割的裂隙之间的RMS误差为1.96 +/- 0.71 mm，手动定义和计算机定义的叶区域之间的相似性指数的平均值为0.988。结果表明自动和手动叶分割之间的高度一致。
High-resolution X-ray computed tomography (CT) imaging is routinely used for clinical pulmonary applications. Since lung function varies regionally and because pulmonary disease is usually not uniformly distributed in the lungs, it is useful to study the lungs on a lobe-by-lobe basis. Thus, it is important to segment not only the lungs, but the lobar fissures as well. In this paper, we demonstrate the use of an anatomic pulmonary atlas, encoded with a priori information on the pulmonary anatomy, to automatically segment the oblique lobar fissures. Sixteen volumetric CT scans from 16 subjects are used to construct the pulmonary atlas. A ridgeness measure is applied to the original CT images to enhance the fissure contrast. Fissure detection is accomplished in two stages: an initial fissure search and a final fissure search. A fuzzy reasoning system is used in the fissure search to analyze information from three sources: the image intensity, an anatomic smoothness constraint, and the atlas-based search initialization. Our method has been tested on 22 volumetric thin-slice CT scans from 12 subjects, and the results are compared to manual tracings. Averaged across all 22 data sets, the RMS error between the automatically segmented and manually segmented fissures is 1.96 +/- 0.71 mm and the mean of the similarity indices between the manually defined and computer-defined lobe regions is 0.988. The results indicate a strong agreement between the automatic and manual lobe segmentations.