论文标题
STAN-CT：使用生成对抗网络标准化CT图像
STAN-CT: Standardizing CT Image using Generative Adversarial Network
论文作者
论文摘要
计算机断层扫描（CT）在肺部恶性肿瘤诊断和治疗评估中起着重要作用，并促进精确医学的递送。但是，使用个性化成像协议在大规模跨中心CT图像放射线研究中构成了挑战。我们提出了一种称为CT图像标准化和归一化的称为STAN-CT的端到端解决方案，该解决方案有效地降低了由于使用不同的成像协议或使用具有相同成像协议的不同CT扫描仪引起的图像特征中的差异。 STAN-CT由两个组成部分组成：1）一种新型的生成对抗网络（GAN）模型，该模型能够有效地学习具有几轮发电机培训的标准成像协议的数据分布，以及2）具有自动DICOM重建管道，具有系统的系统图像质量控制，以确保高素质标准DICOM产生高素质DICOM图像。实验结果表明，与最先进的CT图像标准化和归一化算法相比，STAN-CT的训练效率和模型性能已得到显着提高。
Computed tomography (CT) plays an important role in lung malignancy diagnostics and therapy assessment and facilitating precision medicine delivery. However, the use of personalized imaging protocols poses a challenge in large-scale cross-center CT image radiomic studies. We present an end-to-end solution called STAN-CT for CT image standardization and normalization, which effectively reduces discrepancies in image features caused by using different imaging protocols or using different CT scanners with the same imaging protocol. STAN-CT consists of two components: 1) a novel Generative Adversarial Networks (GAN) model that is capable of effectively learning the data distribution of a standard imaging protocol with only a few rounds of generator training, and 2) an automatic DICOM reconstruction pipeline with systematic image quality control that ensure the generation of high-quality standard DICOM images. Experimental results indicate that the training efficiency and model performance of STAN-CT have been significantly improved compared to the state-of-the-art CT image standardization and normalization algorithms.
