A differential geometric approach to automated segmentation of human airway tree.
A differential geometric approach to automated segmentation of human airway tree.
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DOI：
10.1109/tmi.2010.2076300
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发表时间：
2011-02
影响因子：
10.6
通讯作者：
Gur D
中科院分区：
文献类型：
--
作者：
Pu J;Fuhrman C;Good WF;Sciurba FC;Gur D
气道疾病通常与可能影响肺生理的形态学变化相关。气道的准确表征可能有助于定量评估预后和监测治疗效果。所获得的信息还可以提供对各种肺部疾病的潜在机制的洞察。我们开发了一个计算机化的计划，自动分割的三维人体气道树描绘的CT图像。该方法利用主曲率和主方向在几何空间中区分气道与其他组织。一个“益智游戏”的过程是用来识别假阴性区域，减少假阳性区域，不符合形状分析标准。部分容积效应对小气道检测的负面影响通过对在多个等值（阈值）处建模的肺解剖结构重复所开发的微分几何分析而部分地减轻。除了具有诸如完全自动化、容易实现和对图像噪声和/或伪影相对不敏感的优点之外，该方案实际上没有泄漏问题，并且可以容易地扩展到其他管状类型结构（例如，血管树）。该方案的性能进行了定量评估，使用75个胸部CT检查获得的45名受试者不同的切片厚度，并使用20个公开可用的测试用例，最初设计用于评估不同的气道树分割算法的性能。
Airway diseases are frequently associated with morphological changes that may affect the physiology of the lungs. Accurate characterization of airways may be useful for quantitatively assessing prognosis and for monitoring therapeutic efficacy. The information gained may also provide insight into the underlying mechanisms of various lung diseases. We developed a computerized scheme to automatically segment the three-dimensional human airway tree depicted on CT images. The method takes advantage of both principal curvatures and principal directions in differentiating airways from other tissues in geometric space. A “puzzle game” procedure is used to identify false negative regions and reduce false positive regions that do not meet the shape analysis criteria. The negative impact of partial volume effects on small airway detection is partially alleviated by repeating the developed differential geometric analysis on lung anatomical structures modeled at multiple iso-values (thresholds). In addition to having advantages, such as full automation, easy implementation and relative insensitivity to image noise and/or artifacts, this scheme has virtually no leakage issues and can be easily extended to the extraction or the segmentation of other tubular type structures (e.g., vascular tree). The performance of this scheme was assessed quantitatively using 75 chest CT examinations acquired on 45 subjects with different slice thicknesses and using 20 publicly available test cases that were originally designed for evaluating the performance of different airway tree segmentation algorithms.