Automated detection of polyps with CT colonography:: Evaluation of volumetric features for reduction of false-positive findings
Automated detection of polyps with CT colonography:: Evaluation of volumetric features for reduction of false-positive findings
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DOI：
10.1016/s1076-6332(03)80184-8
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发表时间：
2002-04-01
影响因子：
4.8
通讯作者：
Yoshida, H
中科院分区：
文献类型：
--
作者：
Näppi, J;Yoshida, H
基本原理和目标。为了实现计算机断层扫描（CT）结肠镜息肉计算机辅助诊断（CAD）的高性能，作者(a)开发了新的梯度浓度和定向梯度浓度（DGC）特征，用于区分作者CAD方案产生的真阳性和假阳性（FP）结果，(b)使用受试者工作特征（ROC）分析来量化这些和其他体积特征的区分性能。材料与方法。对43例俯卧位和仰卧位患者行螺旋CT结肠镜检查；I例患者有12个息肉。利用6个体积特征的9个统计量对所生成的候选息肉进行特征化，得到的54个特征统计量通过线性或二次判别分类器进行组合。通过ROC分析和CAD方案的FP率，采用循环法检测识别效果。形状指数（SI）的平均值获得最高的个体ROC表现（曲线下面积= 0.92）。其中，SI和DGC的平均值以及CT值的方差具有较高的ROC性能（曲线下面积= 0.95）。使用二次分类器，基于病例（数据集）分析的敏感性和FP率为100%(95%)，每个患者有2.4个FP发现（每个数据集有1.7个FP发现）。SI和DGC的平均值与CT值的方差相结合，在不牺牲灵敏度的情况下显著降低FP率。这三个特征可能有助于提高作者的CAD方案在CT结肠镜下检测息肉的性能。
Rationale and Objectives. To achieve high performance in computer-assisted diagnosis (CAD) of polyps with computed tomographic (CT) colonography, the authors (a) developed new gradient concentration and directional gradient concentration (DGC) features for differentiating between the true-positive and false-positive (FP) findings generated by the authors' CAD scheme, and (b) used receiver operating characteristic (ROC) analysis to quantify the differentiation performance of these and other volumetric features.Materials and Methods. CT colonography was performed in 43 patients prone and supine with a helical CT scanner; there were 12 polyps in I I patients. The polyp candidates generated by the authors' CAD scheme were characterized by nine statistics of six volumetric features, and the resulting 54 feature statistics were combined by a linear or quadratic discriminant classifier. The discrimination performance was measured with round-robin method by ROC analysis and the FP rate of the CAD scheme.Results. The mean value of shape index (SI) yielded the highest individual ROC performance (area under the curve = 0.92). Among combinations, the mean values of SI and DGC and the variance of CT value yielded a high ROC performance (area under the curve = 0.95). With quadratic classifier, the sensitivity and FP rate of the case-based (data set-based) analysis was 100% (95%) with 2.4 FP findings per patient (1.7 FP findings per data set), respectively.Conclusion. Combination of the mean values of SI and DGC and the variance of CT value reduced the FP rate substantially without sacrificing sensitivity. These three features are potentially useful in improving the performance of the authors' CAD scheme for detecting polyps with CT colonography.