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.

中文翻译：

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使用生成对抗网络的肺结节三维计算机断层扫描图像的属性引导图像生成

目的

要开发和评估使用生成对抗网络（GAN）的肺结节的计算机断层扫描（CT）图像的三维（3D）生成模型。为了指导GAN，使用了肺结节大小。

材料和方法

使用了公共的肺结节CT数据集，从中获得了1182个肺结节。我们提出的GAN模型使用遮罩的3D CT图像和结节大小信息生成图像。为了评估生成的CT图像，两名放射科医生目视评估了肺结节的CT图像是真实的还是生成的，并使用接收器操作特征分析和曲线下面积（AUC）评估了诊断能力。然后，训练了两个将结节大小分为五类的模型，一个使用真实模型，另一个使用生成的肺结节CT图像。使用真实的CT图像，为两个分类模型计算真实的肺结节大小的分类精度。

结果

两名放射科医师的敏感性，特异性和AUC分别如下：放射科医师1：81.3％，37.7％和0.592；放射科医生2：77.1％，30.2％和0.597。对于结节大小的分类，使用真实CT图像构建的分类模型的平均准确度为85％（范围83.2-86.1％），而生成CT图像的分类模型的平均准确度为85％（范围82.2-88.1％）。

结论

我们的结果表明，有可能生成肺结节的3D CT图像，该图像可用于构建没有真实CT图像的肺结节大小分类模型。