Automated detection of lung nodules in CT scans: Preliminary results
Automated detection of lung nodules in CT scans: Preliminary results
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DOI：
10.1118/1.1387272
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发表时间：
2001-08-01
期刊：
影响因子：
3.8
通讯作者：
MacMahon, H
中科院分区：
文献类型：
--
作者：
Armato, SG;Giger, ML;MacMahon, H
我们已经开发出一种全自动计算机化的方法，用于检测肺部结节的螺旋计算机断层扫描（CT）扫描的胸部。该方法基于对诊断CT扫描期间采集的图像数据的二维和三维分析。肺分割在逐部分的基础上进行，以构建分割的肺体积，在该肺体积内执行进一步分析。将多个灰度阈值应用于分割的肺体积以创建一系列阈值化的肺体积。使用18点连接方案来识别每个阈值化肺体积内的连续三维结构，并且选择满足体积标准的那些结构作为初始肺结节候选者。对每个结节候选者计算形态学和灰度特征，在应用基于规则的方法以大大减少对应于非结节的结节候选者的数量之后，通过线性判别分析合并剩余候选者的特征。自动化的方法被应用到43个诊断胸部CT扫描的数据库。受试者工作特征（ROC）分析用于评估分类器区分与假阳性候选者对应的实际结节候选者的能力。该分类任务的ROC曲线下面积在逐例留一评估期间达到0.90的值。自动化的方法产生了一个整体结节检测灵敏度为70%，平均1.5个假阳性检测时，适用于完整的43例数据库。一个相应的结节，检测灵敏度为89%，平均1.3假阳性检测每节实现了一个子集的20例，每例仅包含一个或两个结节。(C)2001年美国医学物理学家协会。
We have developed a fully automated computerized method for the detection of lung nodules in helical computed tomography (CT) scans of the thorax. This method is based on two-dimensional and three-dimensional analyses of the image data acquired during diagnostic CT scans. Lung segmentation proceeds on a section-by-section basis to construct a segmented lung volume within which further analysis is performed. Multiple gray-level thresholds are applied to the segmented lung volume to create a series of thresholded lung volumes. An 18-point connectivity scheme is used to identify contiguous three-dimensional structures within each thresholded lung volume, and those structures that satisfy a volume criterion are selected as initial lung nodule candidates. Morphological and gray-level features are computed for each nodule candidate, After a rule-based approach is applied to greatly reduce the number of nodule candidates that corresponds to non-nodules, the features of remaining candidates are merged through linear discriminant analysis. The automated method was applied to a database of 43 diagnostic thoracic CT scans. Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate nodule candidates that correspond to actual nodules from false-positive candidates. The area under the ROC curve for this categorization task attained a value of 0.90 during leave-one-out-by-case evaluation. The automated method yielded an overall nodule detection sensitivity of 70% with an average of 1.5 false-positive detections per section when applied to the complete 43-case database. A corresponding nodule, detection sensitivity of 89% with an average of 1.3 false-positive detections per section was achieved with a subset of 20 cases that contained only one or two nodules per case. (C) 2001 American Association of Physicists in Medicine.