论文标题
使用深度学习的结构性约束虚拟组织学染色，用于人类冠状动脉成像
Structural constrained virtual histology staining for human coronary imaging using deep learning
论文作者
论文摘要
组织病理学分析对于冠状动脉疾病（CAD）的动脉表征至关重要。但是，组织学需要一个侵入性且耗时的过程。在本文中，我们建议使用光学相干断层扫描（OCT）图像生成虚拟组织学染色，以实现实时的组织学可视化。我们开发了一个深度学习网络，即冠状动脉，将冠状动脉OCT图像转移到虚拟组织学图像中。在对冠状动脉OCT图像中的结构约束方面进行了特殊考虑，我们的方法比基于GAN的常规方法实现了更好的图像生成性能。实验结果表明，冠状动脉生成类似于真实组织学图像的虚拟组织学图像，揭示了人类冠状动脉层。
Histopathological analysis is crucial in artery characterization for coronary artery disease (CAD). However, histology requires an invasive and time-consuming process. In this paper, we propose to generate virtual histology staining using Optical Coherence Tomography (OCT) images to enable real-time histological visualization. We develop a deep learning network, namely Coronary-GAN, to transfer coronary OCT images to virtual histology images. With a special consideration on the structural constraints in coronary OCT images, our method achieves better image generation performance than the conventional GAN-based method. The experimental results indicate that Coronary-GAN generates virtual histology images that are similar to real histology images, revealing the human coronary layers.
