Automated lung segmentation for thoracic CT: Impact on computer-aided diagnosis
Automated lung segmentation for thoracic CT: Impact on computer-aided diagnosis
复制标题
DOI：
10.1016/j.acra.2004.06.005
复制
发表时间：
2004-09-01
影响因子：
4.8
通讯作者：
Sensakovic, WF
中科院分区：
文献类型：
--
作者：
Armato, SG;Sensakovic, WF
基本原理和目标。胸部计算机断层扫描中的自动肺分割对于计算机辅助诊断（CAD）方法的发展至关重要。核心分割方法可用于一般应用，然而，可能需要修改特定的临床任务。材料与方法。一种自动肺分割方法已被应用(1)作为自动肺结节检测的预处理，(2)作为计算机辅助胸膜间皮瘤肿瘤厚度测量的基础。核心方法使用灰度阈值分割肺在每个计算机断层扫描切片。通过沿前连接线分离左右肺，从肺分割区域消除气管和主支气管，抑制膈肌，修改分割。结节检测所需的分割修改包括滚动球算法，以包括胸膜旁结节和形态侵蚀，以消除分割区域边界的部分体积像素。对于自动肺结节检测，修改核心分割方法后，82个实际结节中有4个（4.9%）被排除在肺分割区域之外，未修改的14个结节（17.1%）被排除在外。单独使用核心分割法时，计算机辅助间皮瘤定量方法与134次人工测量的相关系数为0.990；分割修改后，相关系数降至0.977。适用于肺结节检测任务，应用于间皮瘤测量任务。不同的CAD应用对肺自动分割过程有不同的要求。肺分割的具体方法必须适应特定的CAD任务。
Rationale and Objectives. Automated lung segmentation in thoracic computed tomography scans is essential for the development of computer-aided diagnostic (CAD) methods. A core segmentation method may be developed for general application-, however, modifications may be required for specific clinical tasks.Materials and Methods. An automated lung segmentation method has been applied (1) as preprocessing for automated lung nodule detection and (2) as the foundation for computer- assisted measurements of pleural mesothelioma tumor thickness. The core method uses gray-level thresholding to segment the lungs within each computed tomography section. The segmentation is revised through separation of right and left lungs along the anterior junction line, elimination of the trachea and main bronchi from the lung segmentation regions, and suppression of the diaphragm. Segmentation modifications required for nodule detection include a rolling ball algorithm to include juxtapleural nodules and morphologic erosion to eliminate partial volume pixels at the boundary of the segmentation regions.Results. For automated luna nodule detection, 4 of 82 actual nodules (4.9%) were excluded from the lung segmentation regions when the core segmentation method was modified compared with 14 nodules (17.1%) excluded without modifications. The computer- assisted quantification of mesothelioma method achieved a correlation coefficient of 0.990 with 134 manual measurements when the core segmentation method was used alone; correlation was reduced to 0.977 when the segmentation modifications. as adapted for the lung nodule detection task, were applied to the mesothelioma measurement task.Conclusion. Different CAD applications impose different requirements on the automated lung segmentation process. The specific approach to lung segmentation must be adapted to the particular CAD task.