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首页> 美国卫生研究院文献>BioMed Research International >Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks
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Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks

机译：使用由生成对抗网络训练的深卷积神经网络在计算机断层扫描图像中自动进行肺结节分类

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摘要

Lung cancer is a leading cause of death worldwide. Although computed tomography (CT) examinations are frequently used for lung cancer diagnosis, it can be difficult to distinguish between benign and malignant pulmonary nodules on the basis of CT images alone. Therefore, a bronchoscopic biopsy may be conducted if malignancy is suspected following CT examinations. However, biopsies are highly invasive, and patients with benign nodules may undergo many unnecessary biopsies. To prevent this, an imaging diagnosis with high classification accuracy is essential. In this study, we investigate the automated classification of pulmonary nodules in CT images using a deep convolutional neural network (DCNN). We use generative adversarial networks (GANs) to generate additional images when only small amounts of data are available, which is a common problem in medical research, and evaluate whether the classification accuracy is improved by generating a large amount of new pulmonary nodule images using the GAN. Using the proposed method, CT images of 60 cases with confirmed pathological diagnosis by biopsy are analyzed. The benign nodules assessed in this study are difficult for radiologists to differentiate because they cannot be rejected as being malignant. A volume of interest centered on the pulmonary nodule is extracted from the CT images, and further images are created using axial sections and augmented data. The DCNN is trained using nodule images generated by the GAN and then fine-tuned using the actual nodule images to allow the DCNN to distinguish between benign and malignant nodules. This pretraining and fine-tuning process makes it possible to distinguish 66.7% of benign nodules and 93.9% of malignant nodules. These results indicate that the proposed method improves the classification accuracy by approximately 20% in comparison with training using only the original images.
机译：肺癌是世界范围内主要的死亡原因。尽管计算机断层扫描（CT）检查经常用于肺癌诊断，但仅凭CT图像很难区分良性和恶性肺结节。因此，如果在CT检查后怀疑有恶性肿瘤，可以进行支气管镜活检。然而，活检是高度侵入性的，良性结节患者可能会进行许多不必要的活检。为了防止这种情况，具有高分类精度的成像诊断是必不可少的。在这项研究中，我们使用深度卷积神经网络（DCNN）研究了CT图像中肺结节的自动分类。当只有少量数据可用时，我们使用生成对抗网络（GANs）生成其他图像，这是医学研究中的常见问题，并通过使用生成大量新的肺结节图像来评估分类准确性是否得到了改善甘使用提出的方法，对经活检证实病理诊断的60例CT图像进行分析。本研究中评估的良性结节难以让放射科医生区分，因为它们不能被认为是恶性的。从CT图像中提取以肺结节为中心的感兴趣的体积，并使用轴向截面和增强的数据创建其他图像。使用GAN生成的结节图像训练DCNN，然后使用实际的结节图像进行微调，以允许DCNN区分良性和恶性结节。这种预训练和微调过程可以区分出66.7％的良性结节和93.9％的恶性结节。这些结果表明，与仅使用原始图像进行训练相比，该方法将分类准确性提高了约20％。

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