Attribute-guided image generation of three-dimensional computed tomography images of lung nodules using a generative adversarial network
Attribute-guided image generation of three-dimensional computed tomography images of lung nodules using a generative adversarial network
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DOI：
10.1016/j.compbiomed.2020.104032
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发表时间：
2020-11-01
影响因子：
7.7
通讯作者：
Fujita, Hiroshi
中科院分区：
文献类型：
--
作者：
Nishio, Mizuho;Muramatsu, Chisako;Fujita, Hiroshi
目的：使用生成对抗网络（GAN）开发和评价肺结节计算机断层扫描（CT）图像的三维（3D）生成模型。材料和方法：使用公共的肺结节CT数据集，从其中获得1182个肺结节。我们提出的GAN模型使用掩蔽的3D CT图像和结节大小信息来生成图像。为了评价生成的CT图像，由两名放射科医师目视评价肺结节CT图像是否真实或生成，并使用受试者操作特征分析和曲线下面积（AUC）评价诊断能力。然后，训练用于将结节大小分类为五个类别的两个模型，一个使用真实的，另一个使用生成的肺结节CT图像。结果：两种分类模型的敏感性、特异性和AUC分别为：放射科医师1：81.3%，37.7%，0.592;放射科医师2：77.1%，30.2%，0.597。对于结节大小的分类，用真实CT图像构建的分类模型的平均准确率为85%（范围83.2-86.1%），生成的CT图像为85%（范围82.2-88.1%）。我们的研究结果表明，有可能生成肺结节的3D CT图像，该图像可用于构建肺结节大小的分类模型，而无需真实的CT图像
Purpose: To develop and evaluate a three-dimensional (3D) generative model of computed tomography (CT) images of lung nodules using a generative adversarial network (GAN). To guide the GAN, lung nodule size was used.Materials and methods: A public CT dataset of lung nodules was used, from where 1182 lung nodules were obtained. Our proposed GAN model used masked 3D CT images and nodule size information to generate images. To evaluate the generated CT images, two radiologists visually evaluated whether the CT images with lung nodule were true or generated, and the diagnostic ability was evaluated using receiver-operating characteristic analysis and area under the curves (AUC). Then, two models for classifying nodule size into five categories were trained, one using the true and the other using the generated CT images of lung nodules. Using true CT images, the classification accuracy of the sizes of the true lung nodules was calculated for the two classification models.Results: The sensitivity, specificity, and AUC of the two radiologists were respectively as follows: radiologist 1: 81.3%, 37.7%, and 0.592; radiologist 2: 77.1%, 30.2%, and 0.597. For categorization of nodule size, the mean accuracy of the classification model constructed with true CT images was 85% (range 83.2-86.1%), and that with generated CT images was 85% (range 82.2-88.1%).Conclusions: Our results show that it was possible to generate 3D CT images of lung nodules that could be used to construct a classification model of lung nodule size without true CT images.