Department of Radiology, Duke University, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705, USA.
Med Phys. 2009 Oct;36(10):4592-9. doi: 10.1118/1.3222872.
Accurate 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.
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 (dmax) between the automatically segmented lungs and the reference lungs were employed to evaluate the performance of the segmentation method.
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 dmax 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 dmax 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 dmax of 11.71 mm for the 45 abnormal cases with ILD.
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.
在胸部计算机断层扫描（CT）中对患有严重间质性肺病（ILD）的肺部进行准确分割是计算机辅助诊断（CAD）系统开发中的一项重要而困难的任务。因此，我们在这项研究中开发了一种基于纹理分析的方法，用于对多排 CT 扫描中患有严重 ILD 的肺部进行准确分割。
我们的数据库由 76 例 CT 扫描组成，包括 31 例正常病例和 45 例中重度 ILD 异常病例。为每位 CT 扫描选择三个切片，首先由医学物理学家手动勾画肺区，然后由胸部放射科专家确认或修改，将其作为肺分割的参考标准。为了分割肺区，我们首先采用 CT 值阈值技术获得初始肺区估计，包括正常和轻度 ILD 肺实质。然后，我们使用共生矩阵导出的纹理特征图像进一步识别严重 ILD 的异常肺区。最后，我们将识别出的异常肺区与初始肺区相结合，生成最终的肺区分割结果。我们采用重叠率、体积一致性、平均绝对距离（MAD）和最大绝对距离（dmax）来评估分割方法的性能。
我们的分割方法对数据库中的所有病例的平均重叠率为 96.7%，平均体积一致性为 98.5%，平均 MAD 为 0.84mm，平均 dmax 为 10.84mm；对 31 例正常病例的平均重叠率为 97.7%，平均体积一致性为 99.0%，平均 MAD 为 0.66mm，平均 dmax 为 9.59mm；对患有 ILD 的 45 例异常病例的平均重叠率为 96.1%，平均体积一致性为 98.1%，平均 MAD 为 0.96mm，平均 dmax 为 11.71mm。
我们的肺分割方法为患有严重 ILD 的异常 CT 扫描提供了准确的分割结果，将有助于开发 ILD 定量、检测和诊断的 CAD 系统。
