Lu Lin, Tan Yongqiang, Schwartz Lawrence H, Zhao Binsheng
Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, New York 10032.
Med Phys. 2015 Sep;42(9):5042-54. doi: 10.1118/1.4927573.
The diversity of lung nodules poses difficulty for the current computer-aided diagnostic (CAD) schemes for lung nodule detection on computed tomography (CT) scan images, especially in large-scale CT screening studies. We proposed a novel CAD scheme based on a hybrid method to address the challenges of detection in diverse lung nodules.
The hybrid method proposed in this paper integrates several existing and widely used algorithms in the field of nodule detection, including morphological operation, dot-enhancement based on Hessian matrix, fuzzy connectedness segmentation, local density maximum algorithm, geodesic distance map, and regression tree classification. All of the adopted algorithms were organized into tree structures with multi-nodes. Each node in the tree structure aimed to deal with one type of lung nodule.
The method has been evaluated on 294 CT scans from the Lung Image Database Consortium (LIDC) dataset. The CT scans were randomly divided into two independent subsets: a training set (196 scans) and a test set (98 scans). In total, the 294 CT scans contained 631 lung nodules, which were annotated by at least two radiologists participating in the LIDC project. The sensitivity and false positive per scan for the training set were 87% and 2.61%. The sensitivity and false positive per scan for the testing set were 85.2% and 3.13%.
The proposed hybrid method yielded high performance on the evaluation dataset and exhibits advantages over existing CAD schemes. We believe that the present method would be useful for a wide variety of CT imaging protocols used in both routine diagnosis and screening studies.
肺结节的多样性给当前基于计算机断层扫描（CT）图像的肺结节检测计算机辅助诊断（CAD）方案带来了困难，尤其是在大规模CT筛查研究中。我们提出了一种基于混合方法的新型CAD方案，以应对不同肺结节检测的挑战。
本文提出的混合方法整合了结节检测领域中几种现有的且广泛使用的算法，包括形态学运算、基于Hessian矩阵的点增强、模糊连接分割、局部密度最大值算法、测地距离图和回归树分类。所有采用的算法都被组织成具有多个节点的树结构。树结构中的每个节点旨在处理一种类型的肺结节。
该方法在来自肺部影像数据库联盟（LIDC）数据集的294例CT扫描上进行了评估。CT扫描被随机分为两个独立的子集：训练集（196例扫描）和测试集（98例扫描）。总共，这294例CT扫描包含631个肺结节，这些结节由至少两名参与LIDC项目的放射科医生进行了标注。训练集的敏感度和每扫描假阳性率分别为87%和2.61%。测试集的敏感度和每扫描假阳性率分别为85.2%和3.13%。
所提出的混合方法在评估数据集上表现出高性能，并且比现有的CAD方案具有优势。我们相信本方法将对常规诊断和筛查研究中使用的各种CT成像方案有用。
