基于生成对抗网络的CT图像数据扩增

训练基于深度学习的计算机辅助诊断系统可以有效地从肺部CT图像中检测出是否受到COVID-19感染, 但目前面临的主要问题是缺乏高质量带标注的CT图像用于训练. 为了有效的解决该问题, 本文提出了一种基于生成对抗网络来扩增肺部CT图像的方法. 新方法通过生成不同感染区域的标签并通过泊松融合以增加生成图像的多样性; 通过训练对抗网络模型实现图像的转换生成, 以达到扩增CT图像的目的. 为验证生成数据的有效性, 基于扩增数据进一步做了分割实验. 通过图像生成实验和分割实验, 结果都表明, 本文提出的图像生成方法取得了较好的效果.

CT Image Data Amplification Based on Generative Adversarial Network

Whether the lung is infected by COVID-19 can be effectively detected from lung computed tomography (CT) images by the computer-aided diagnosis system whose training is based on deep learning. However, the main problem is the lack of high-quality labeled CT images available for training. This study proposes a method of augmenting lung CT images with the generative adversarial network (GAN). Specifically, labels of different infected areas are generated, and Poisson fusion is performed to enhance the diversity of the generated images. Then, image transformation and generation are implemented by training the GAN model to fulfill the purpose of augmenting the CT image. Segmentation experiments based on the augmented data are also carried out to verify the effectiveness of the data generated. The results of the image generation and segmentation experiments both show that the proposed image generation method achieves favorable effects.