Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications
Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications
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DOI：
10.1118/1.2804558
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发表时间：
2007-12-01
期刊：
影响因子：
3.8
通讯作者：
Kazerooni, Ella A.
中科院分区：
文献类型：
--
作者：
Zhou, Chuan;Chan, Heang-Ping;Kazerooni, Ella A.
作者正在开发一种计算机化肺血管分割方法，用于计算机断层肺血管造影 (CTPA) 图像上的计算机辅助肺栓塞 (PE) 检测系统。由于 PE 仅发生在肺动脉内部，因此在 3D CTPA 图像中自动准确地分割肺血管是 PE CAD 系统的重要步骤。为了分割肺部内的肺血管，首先使用期望最大化（EM）分析和形态学操作提取肺部区域。作者基于对多个尺度 Hessian 矩阵特征值的分析，开发了一种 3D 多尺度滤波技术来增强肺血管结构。滤波器的新响应函数旨在增强包括血管分叉在内的所有血管结构，并抑制非血管结构，例如血管周围的淋巴组织。然后使用 EM 估计通过提取每个尺度的高响应体素来分割血管结构。最终通过基于“连通分量”分析整合所有尺度的分段血管来重建血管树。使用两个包含 PE 的 CTPA 案例来评估系统的性能。这两例病例中，其中一例还患有胸腔积液疾病。两位经验丰富的胸部放射科医生通过手动跟踪动脉树并使用计算机图形用户界面标记血管中心，提供了包括动脉和静脉在内的肺血管的黄金标准。血管树分割的准确性通过“黄金标准”血管中心点与分割血管重叠的百分比来评估。结果显示，在没有和患有其他肺部疾病的情况下，手动标记的动脉中心点有 96.2% (2398/2494) 和 96.3% (1910/1984) 与分段血管重叠。对于包括动脉和静脉在内的所有血管中手动标记的中心点，对于不存在其他肺部疾病和患有其他肺部疾病的情况，分割准确率分别为97.0％（4546/4689）和93.8％（4439/4732）。由于缺乏血管的真实情况，除了对血管分割性能进行定量评估外，还进行了目视检查来评估分割效果。结果表明，在这些测试案例中，使用我们的方法进行的血管分割可以准确地提取肺血管，并且不会因 PE 闭塞而导致血管退化。 (C) 2007 年美国医学物理学家协会。
The authors are developing a computerized pulmonary vessel segmentation method for a computer-aided pulmonary embolism (PE) detection system on computed tomographic pulmonary angiography (CTPA) images. Because PE only occurs inside pulmonary arteries, an automatic and accurate segmentation of the pulmonary vessels in 3D CTPA images is an essential step for the PE CAD system. To segment the pulmonary vessels within the lung, the lung regions are first extracted using expectation-maximization (EM) analysis and morphological operations. The authors developed a 3D multiscale filtering technique to enhance the pulmonary vascular structures based on the analysis of eigenvalues of the Hessian matrix at multiple scales. A new response function of the filter was designed to enhance all vascular structures including the vessel bifurcations and suppress nonvessel structures such as the lymphoid tissues surrounding the vessels. An EM estimation is then used to segment the vascular structures by extracting the high response voxels at each scale. The vessel tree is finally reconstructed by integrating the segmented vessels at all scales based on a "connected component" analysis. Two CTPA cases containing PEs were used to evaluate the performance of the system. One of these two cases also contained pleural effusion disease. Two experienced thoracic radiologists provided the gold standard of pulmonary vessels including both arteries and veins by manually tracking the arterial tree and marking the center of the vessels using a computer graphical user interface. The accuracy of vessel tree segmentation was evaluated by the percentage of the "gold standard" vessel center points overlapping with the segmented vessels. The results show that 96.2% (2398/2494) and 96.3% (1910/1984) of the manually marked center points in the arteries overlapped with segmented vessels for the case without and with other lung diseases. For the manually marked center points in all vessels including arteries and veins, the segmentation accuracy are 97.0% (4546/4689) and 93.8% (4439/4732) for the cases without and with other lung diseases, respectively. Because of the lack of ground truth for the vessels, in addition to quantitative evaluation of the vessel segmentation performance, visual inspection was conducted to evaluate the segmentation. The results demonstrate that vessel segmentation using our method can extract the pulmonary vessels accurately and is not degraded by PE occlusion to the vessels in these test cases. (C) 2007 American Association of Physicists in Medicine.