Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique.
Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique.
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DOI：
10.1118/1.2799885
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发表时间：
2007-12
期刊：
影响因子：
3.8
通讯作者：
Jiahui Wang;R. Engelmann;Qiang Li
中科院分区：
文献类型：
--
作者：
Jiahui Wang;R. Engelmann;Qiang Li
CT图像中肺结节的准确分割是肺癌计算机辅助诊断的一个重要而困难的课题。因此，作者开发了一种新的自动化方法，用于在三维（3D）CT中准确分割结节。首先，在结节的位置处确定感兴趣体积（VOI）。为了简化结节分割，通过使用关键的“螺旋扫描”技术，将3D VOI转换为二维（2D）图像，其中源自VOI中心的多条径向线从“北极”到“南极”扫描VOI。“放射线扫描的体素提供了一个变换后的2D图像。由于3D图像中的结节表面在变换后的2D图像中变成曲线，因此螺旋扫描技术大大简化了分割方法，并且能够获得可靠的分割结果。采用动态规划技术描绘2D图像中结节的“最佳”轮廓，其对应于3D图像中结节的表面。然后将最佳轮廓转换回3D图像空间以提供结节的表面。由计算机和放射科医生提供的结节区域之间的重叠被用作评估分割方法的性能度量。该数据库包括两个肺部成像数据库联盟（LIDC）数据集，分别包含23和86次CT扫描，其中23和73个结节直径≥ 3 mm。对于这两个数据集，分别由六名和四名放射科医生手动勾画结节轮廓，作为结节分割性能评价中的参考标准。分割方法在第一个数据集上进行训练，并在第二个LIDC数据集上进行测试。平均重叠值分别为66%和64%的结节在第一和第二LIDC数据集，分别代表了更高的性能水平比两个现有的分割方法，也通过使用LIDC数据集进行了评估。该分割方法为肺结节的分割提供了相对可靠的结果，将有助于肺癌的定量、检测和诊断。
Accurate segmentation of pulmonary nodules in computed tomography (CT) is an important and difficult task for computer-aided diagnosis of lung cancer. Therefore, the authors developed a novel automated method for accurate segmentation of nodules in three-dimensional (3D) CT. First, a volume of interest (VOI) was determined at the location of a nodule. To simplify nodule segmentation, the 3D VOI was transformed into a two-dimensional (2D) image by use of a key "spiral-scanning" technique, in which a number of radial lines originating from the center of the VOI spirally scanned the VOI from the "north pole" to the "south pole." The voxels scanned by the radial lines provided a transformed 2D image. Because the surface of a nodule in the 3D image became a curve in the transformed 2D image, the spiral-scanning technique considerably simplified the segmentation method and enabled reliable segmentation results to be obtained. A dynamic programming technique was employed to delineate the "optimal" outline of a nodule in the 2D image, which corresponded to the surface of the nodule in the 3D image. The optimal outline was then transformed back into 3D image space to provide the surface of the nodule. An overlap between nodule regions provided by computer and by the radiologists was employed as a performance metric for evaluating the segmentation method. The database included two Lung Imaging Database Consortium (LIDC) data sets that contained 23 and 86 CT scans, respectively, with 23 and 73 nodules that were 3 mm or larger in diameter. For the two data sets, six and four radiologists manually delineated the outlines of the nodules as reference standards in a performance evaluation for nodule segmentation. The segmentation method was trained on the first and was tested on the second LIDC data sets. The mean overlap values were 66% and 64% for the nodules in the first and second LIDC data sets, respectively, which represented a higher performance level than those of two existing segmentation methods that were also evaluated by use of the LIDC data sets. The segmentation method provided relatively reliable results for pulmonary nodule segmentation and would be useful for lung cancer quantification, detection, and diagnosis.