Automated segmentation of lungs with severe interstitial lung disease in CT
Automated segmentation of lungs with severe interstitial lung disease in CT
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DOI：
10.1118/1.3222872
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发表时间：
2009-10-01
期刊：
影响因子：
3.8
通讯作者：
Li, Qiang
中科院分区：
文献类型：
--
作者：
Wang, Jiahui;Li, Feng;Li, Qiang
目的：在胸部CT中准确分割严重间质性肺疾病(ILD)的肺部是计算机辅助诊断(CAD)系统开发中的一项重要而困难的任务。因此，本研究开发了一种基于纹理分析的多层螺旋CT扫描中重度ILD肺的准确分割方法。方法：我们的数据库包括76例CT扫描，包括31例正常肺和45例中重度ILD异常病例。每一次CT扫描的三个选定切片上的肺首先由医学物理学家手动勾画，然后由专家胸部放射科医生确认或修订，并作为肺分割的参考标准。为了分割肺部，我们首先使用CT值阈值技术来获得初步的肺估计，包括正常和轻度ILD肺实质。然后，我们使用来自共生矩阵的纹理特征图像来进一步识别严重ILD的异常肺部区域。最后，将识别出的肺异常区域与初始肺相结合，得到最终的肺分割结果。结果：我们的分割方法获得的平均重叠率为96.7%，平均体积符合率为98.5%，平均MAD为0.84 mm，平均d(Max)为10.84 mm；31例正常者的平均重叠率为97.7%，平均体积符合率为99.0%，平均MAD为0.66 mm，平均d(Max)为9.59 mm，45例ILD患者的平均重叠率为96.1%，平均体积符合率为98.1%，平均MAD为0.96 mm，平均d(Max)为11.71 mm。结论：本方法对重度ILD的CT图像提供了准确的分割结果，可用于ILD的定量、检测和诊断的CAD系统的开发。(C)2009年美国医学物理学家协会。[DOI：10.1118/1.3222872]
Purpose: Accura te segmentation of lungs with severe interstitial lung disease (ILD) in thoracic computed tomography (CT) is an important and difficult task in the development of computer-aided diagnosis (CAD) systems. Therefore, we developed in this study a texture analysis-based method for accurate segmentation of lungs with severe ILD in multidetector CT scans.Methods: Our database consisted of 76 CT scans, including 31 normal cases and 45 abnormal cases with moderate or severe ILD. The lungs in three selected slices for each CT scan were first manually delineated by a medical physicist, and then confirmed or revised by an expert chest radiologist, and they were used as the reference standard for lung segmentation. To segment the lungs, we first employed a CT value thresholding technique to obtain an initial lung estimate, including normal and mild ILD lung parenchyma. We then used texture-feature images derived from the co-occurrence matrix to further identify abnormal lung regions with severe ILD. Finally, we combined the identified abnormal lung regions with the initial lungs to generate the final lung segmentation result. The overlap rate, volume agreement, mean absolute distance (MAD), and maximum absolute distance (d(max)) between the automatically segmented lungs and the reference lungs were employed to evaluate the performance of the segmentation method.Results: Our segmentation method achieved a mean overlap rate of 96.7%, a mean volume agreement of 98.5%, a mean MAD of 0.84 mm, and a mean d(max) of 10.84 mm for all the cases in our database; a mean overlap rate of 97.7%, a mean volume agreement of 99.0%, a mean MAD of 0.66 mm, and a mean d(max) of 9.59 mm for the 31 normal cases; and a mean overlap rate of 96.1%, a mean volume agreement of 98.1%, a mean MAD of 0.96 mm, and a mean d(max) of 11.71 mm for the 45 abnormal cases with ILD.Conclusions: Our lung segmentation method provided accurate segmentation results for abnormal CT scans with severe ILD and would be useful for developing CAD systems for quantification, detection, and diagnosis of ILD. (C) 2009 American Association of Physicists in Medicine. [DOI: 10.1118/1.3222872]