论文标题
基于无监督的域适应性COVID-19 CT感染分割网络
Unsupervised domain adaptation based COVID-19 CT infection segmentation network
论文作者
论文摘要
事实证明，计算机断层扫描（CT）图像中感染区域的自动分割已被证明是COVID-19的有效诊断方法。但是，由于像素级注释的医学图像数量有限，因此准确的分割仍然是一个主要挑战。在本文中，我们提出了一个基于无监督的域自适应分割网络，以改善COVID-19 CT图像中感染区域的分割性能。特别是，我们建议利用合成数据，并有限的未标记的实际COVID-19 CT图像共同训练分割网络。此外，我们开发了一个新颖的域适应模块，该模块用于对齐两个域并有效提高分割网络对真实域的概括能力。此外，我们提出了一种无监督的对抗训练方案，该方案鼓励分割网络学习域不变特征，以便可以使用可靠的功能进行分割。实验结果表明，我们的方法可以在COVID-19 CT图像上实现最新的分割性能。
Automatic segmentation of infection areas in computed tomography (CT) images has proven to be an effective diagnosis approach for COVID-19. However, due to the limited number of pixel-level annotated medical images, accurate segmentation remains a major challenge. In this paper, we propose an unsupervised domain adaptation based segmentation network to improve the segmentation performance of the infection areas in COVID-19 CT images. In particular, we propose to utilize the synthetic data and limited unlabeled real COVID-19 CT images to jointly train the segmentation network. Furthermore, we develop a novel domain adaptation module, which is used to align the two domains and effectively improve segmentation network's generalization capability to the real domain. Besides, we propose an unsupervised adversarial training scheme, which encourages the segmentation network to learn the domain-invariant feature, so that the robust feature can be used for segmentation. Experimental results demonstrate that our method can achieve state-of-the-art segmentation performance on COVID-19 CT images.
