Pu Jiantao, Zheng Bin, Leader Joseph Ken, Wang Xiao-Hui, Gur David
Imaging Research Center, Department of Radiology University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA.
Med Phys. 2008 Aug;35(8):3453-61. doi: 10.1118/1.2948349.
The authors present a new computerized scheme to automatically detect lung nodules depicted on computed tomography (CT) images. The procedure is performed in the signed distance field of the CT images. To obtain an accurate signed distance field, CT images are first interpolated linearly along the axial direction to form an isotropic data set. Then a lung segmentation strategy is applied to smooth the lung border aiming to include as many juxtapleural nodules as possible while minimizing over segmentations of the lung regions. Potential nodule regions are then detected by locating local maximas of signed distances in each subvolume with values and the sizes larger than the smallest nodule of interest in the three-dimensional space. Finally, all detected candidates are scored by computing the similarity distance of their medial axis-like shapes obtained through a progressive clustering strategy combined with a marching cube algorithm from a sphere based shape. A free-response receiver operating characteristics curve is computed to assess the scheme performance. A performance test on 52 low-dose CT screening examinations that depict 184 verified lung nodules showed that during the initial stage the scheme achieved an asymptotic maximum sensitivity of 95.1% (175/184) with an average of 1200 suspicious voxels per CT examination. The nine missed nodules included two small solid nodules (with a diameter < or =3.1 mm) and seven nonsolid nodules. The final performance level after the similarity scoring stage was an absolute sensitivity level, namely, including the nine missed during the initial stage, of 81.5% (150/184) with 6.5 false-positive identifications per CT examination. This preliminary study demonstrates the feasibility of applying a simple and robust geometric model using the signed distance field to identify suspicious lung nodules. In the authors' data set the sensitivity of this scheme is not affected by nodule size. In addition to potentially being a stand alone approach, the signed distance field based method can be easily implemented as an initial filtering step in other computer-aided detection schemes.
作者提出了一种新的计算机化方案，用于自动检测计算机断层扫描（CT）图像上显示的肺结节。该过程在CT图像的符号距离场中进行。为了获得准确的符号距离场，首先沿轴向对CT图像进行线性插值，以形成各向同性数据集。然后应用肺分割策略来平滑肺边界，目的是尽可能多地包含胸膜旁结节，同时尽量减少肺区域的过度分割。然后通过在每个子体积中定位符号距离的局部最大值来检测潜在的结节区域，这些局部最大值在三维空间中的值和大小大于感兴趣的最小结节。最后，通过计算通过基于球体形状的渐进聚类策略与移动立方体算法获得的它们的中轴线状形状的相似距离，对所有检测到的候选结节进行评分。计算自由响应接收器操作特性曲线以评估该方案的性能。对52次低剂量CT筛查检查进行的性能测试显示了184个经证实的肺结节，结果表明在初始阶段，该方案实现了95.1%（175/184）的渐近最大灵敏度，每次CT检查平均有1200个可疑体素。九个漏诊结节包括两个小实性结节（直径≤3.1mm）和七个非实性结节。相似性评分阶段后的最终性能水平是绝对灵敏度水平，即包括初始阶段漏诊的九个结节，为81.5%（150/184），每次CT检查有6.5个假阳性识别。这项初步研究证明了应用使用符号距离场的简单而强大的几何模型来识别可疑肺结节的可行性。在作者的数据集中，该方案的灵敏度不受结节大小的影响。除了可能作为一种独立方法外，基于符号距离场的方法可以很容易地作为其他计算机辅助检测方案中的初始过滤步骤来实施。
