论文标题
使用基于补丁的条件生成对抗网络的人工CT图像生成
Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial Networks
论文作者
论文摘要
深度学习具有减轻各种临床程序的诊断和预后的巨大潜力。但是，缺乏足够数量的医学图像是使用深度学习进行基于图像的分析的最常见障碍。由于注释稀缺，自动医学分析中的半监督技术引起了人们的关注。人工数据增强和发电技术（例如生成对抗网络（GAN））可能有助于克服这一障碍。在这项工作中，我们提出了一种图像生成方法，该方法使用具有条件歧视器的生成对抗网络，其中分割面罩被用作图像生成的条件。我们验证了全心计算机断层扫描（CT）图像及其七个子结构的GAN增强医学图像产生的可行性，即：左心室，右心室，左心房，右心房，心肌，肺动脉和主动脉a。获得的结果证明了所提出的对抗方法对于准确生成高质量CT图像的适用性。提出的方法显示出促进人工医学图像生成领域进一步研究的巨大潜力。
Deep learning has a great potential to alleviate diagnosis and prognosis for various clinical procedures. However, the lack of a sufficient number of medical images is the most common obstacle in conducting image-based analysis using deep learning. Due to the annotations scarcity, semi-supervised techniques in the automatic medical analysis are getting high attention. Artificial data augmentation and generation techniques such as generative adversarial networks (GANs) may help overcome this obstacle. In this work, we present an image generation approach that uses generative adversarial networks with a conditional discriminator where segmentation masks are used as conditions for image generation. We validate the feasibility of GAN-enhanced medical image generation on whole heart computed tomography (CT) images and its seven substructures, namely: left ventricle, right ventricle, left atrium, right atrium, myocardium, pulmonary arteries, and aorta. Obtained results demonstrate the suitability of the proposed adversarial approach for the accurate generation of high-quality CT images. The presented method shows great potential to facilitate further research in the domain of artificial medical image generation.
