Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: Comparison with a density mask
Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: Comparison with a density mask
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DOI：
10.2214/ajr.175.5.1751329
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发表时间：
2000-11-01
影响因子：
5
通讯作者：
Thelen, M
中科院分区：
文献类型：
--
作者：
Kauczor, HU;Heitmann, K;Thelen, M
目的：比较多神经网络与密度掩膜在临床条件下对高分辨率CT毛玻璃混浊的自动检测和定量。研究对象和方法。84例患者（男性54例，女性30例，年龄18-82岁，平均年龄49岁）共进行99次连续高分辨率CT扫描。该神经网络设计用于高灵敏度检测毛玻璃混浊，并忽略空气组织界面以增加特异性。将神经网络的结果与密度掩模（阈值，-750/-300 H）的结果进行比较，放射科医生作为金标准。神经网络将肺总面积的6%分类为毛玻璃样混浊。密度掩模未能检测到1.3%，这一百分比代表了神经网络实现的灵敏度增加。密度掩模识别出另外17.3%的肺总面积为神经网络未检测到的毛玻璃混浊。这个区域代表了神经网络实现的特异性增加。与放射科医生分类的毛玻璃混浊的程度有关。神经网络（密度掩模）的灵敏度为99%(89%)，特异性为83%（55%）。阳性预测值78%(18%)，阴性预测值99%(98%)，准确率89%（58%）。神经网络对高分辨率CT毛玻璃混浊的自动分割和定量，足够精确，可用于临床环境下的图像预解释；它优于双阈值密度掩码。
OBJECTIVE, We compared multiple neural networks with a density mask for the automatic detection and quantification of ground-glass opacities on high-resolution CT under clinical conditions.SUBJECTS AND METHODS. Eighty-four patients (54 men and 30 women; age range, 18-82 years; mean age, 49 years) with a total of 99 consecutive high-resolution CT scans were enrolled in the study. The neural network was designed to detect ground-glass opacities with high sensitivity and to omit air-tissue interfaces to increase specificity. The results of the neural network were compared with those of a density mask (thresholds, -750/-300 H), with a radiologist serving as the gold standard.RESULTS. The neural network classified 6% of the total lung area as ground-glass opacities. The density mask failed to detect 1.3%, and this percentage represented the increase in sensitivity that was achieved by the neural network. The density mask identified another 17.3% of the total lung area to be ground-glass opacities that were not detected by the neural network. This area represented the increase in specificity achieved by the neural network. Related to the extent of the ground-glass opacities as classified by the radiologist. the neural network (density mask) reached a sensitivity of 99% (89%), specificity of 83% (55%). positive predictive value of 78% (18%), negative predictive value of 99% (98%), and accuracy of 89% (58%).CONCLUSION. Automatic segmentation and quantification of ground-glass opacities on high-resolution CT by a neural network are sufficiently accurate to be implemented for the preinterpretation of images in a clinical environment; it is superior to a double-threshold density mask.