Computer-aided detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA): performance evaluation with independent data sets.
Computer-aided detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA): performance evaluation with independent data sets.
复制标题
计算机断层肺血管造影 (CTPA) 中肺栓塞的计算机辅助检测：使用独立数据集进行性能评估。
DOI：
10.1118/1.3157102
复制
发表时间：
2009
期刊：
影响因子：
3.8
通讯作者：
Kazerooni,EllaA
中科院分区：
文献类型：
--
作者：
Zhou,Chuan;Chan,Heang-Ping;Sahiner,Berkman;Hadjiiski,LubomirM;Chughtai,Aamer;Patel,Smita;Wei,Jun;Cascade,PhilipN;Kazerooni,EllaA
作者正在开发计算机断层肺血管造影（CTPA）扫描中肺栓塞（PE）的计算机辅助检测系统。使用基于多尺度下Hessian矩阵特征值分析的3D期望最大化分割方法提取肺血管树。一个并行的多重预筛选方法被应用到分割的血管，以确定包含可疑PE的感兴趣的体积（VOIs）。设计了一种具有特征选择的线性判别分析（LDA）分类器，以减少误报（FP）。提取表征PE的对比度、灰度级和大小的特征作为LDA分类器的输入预测变量。经IRB批准，从患者档案中回顾性收集了59例CTPA PE病例（UM病例）。从肺栓塞诊断的前瞻性研究（PIOPED）II临床试验的数据集中随机选择获得访问许可的69例CTPA PE病例。UM和PIOPED病例中存在广泛的肺实质或胸膜疾病。经验丰富的胸放射科医生分别在UM和PIOPED数据集中手动标记595和800 PE作为参考标准。动脉PE闭塞范围为5%至100%，PE位于主肺动脉至亚段动脉水平。在UM病例中发现的595例PE中，分别有245例和350例PE位于亚段动脉和更近端动脉。检测性能通过自由响应ROC（FROC）分析进行评估。FROC分析表明，当用UM病例训练LDA分类器时，PE检测系统对于PIPED病例可以在18.9 FP/病例下实现80%的总体灵敏度。当用PIOPED病例训练LDA分类器时，UM病例的测试灵敏度在22.6 FP/病例下为80%。检测性能取决于PE所在的动脉水平和闭塞百分比。亚段动脉中PE的敏感性低于更近端动脉，并且低于20%闭塞的PE的敏感性更低。结果表明，PE检测系统在PIOPED和UM数据集的独立CTPA扫描上实现了PE检测的高灵敏度，并证明了自动PE检测方法可以推广到未知病例的潜力。
The authors are developing a computer‐aided detection system for pulmonary emboli (PE) in computed tomographic pulmonary angiography (CTPA) scans. The pulmonary vessel tree is extracted using a 3D expectation‐maximization segmentation method based on the analysis of eigenvalues of Hessian matrices at multiple scales. A parallel multiprescreening method is applied to the segmented vessels to identify volume of interests (VOIs) that contained suspicious PE. A linear discriminant analysis (LDA) classifier with feature selection is designed to reduce false positives (FPs). Features that characterize the contrast, gray level, and size of PE are extracted as input predictor variables to the LDA classifier. With the IRB approval, 59 CTPA PE cases were collected retrospectively from the patient files (UM cases). With access permission, 69 CTPA PE cases were randomly selected from the data set of the prospective investigation of pulmonary embolism diagnosis (PIOPED) II clinical trial. Extensive lung parenchymal or pleural diseases were present in UM and PIOPED cases. Experienced thoracic radiologists manually marked 595 and 800 PE as the reference standards in the UM and PIOPED data sets, respectively. PE occlusion of arteries ranged from 5% to 100%, with PE located from the main pulmonary artery to the subsegmental artery levels. Of the 595 PE identified in the UM cases, 245 and 350 PE were located in the subsegmental arteries and the more proximal arteries, respectively. The detection performance was assessed by free response ROC (FROC) analysis. The FROC analysis indicated that the PE detection system could achieve an overall sensitivity of 80% at 18.9 FPs/case for the PIOPED cases when the LDA classifier was trained with the UM cases. The test sensitivity with the UM cases was 80% at 22.6 FPs/cases when the LDA classifier was trained with the PIOPED cases. The detection performance depended on the arterial level where the PE was located and on the percentage of occlusion. The sensitivity was lower for PE in the subsegmental arteries than in more proximal arteries and was lower for PE with less than 20% occlusion. The results indicate that the PE detection system achieves high sensitivity for PE detection on independent CTPA scans for both the PIOPED and UM data sets and demonstrate the potential that the automated PE detection approach can be generalized to unknown cases.