论文标题
弱监督的3D冠状动脉重建来自两视视血管造影图像
Weakly-supervised 3D coronary artery reconstruction from two-view angiographic images
论文作者
论文摘要
冠状动脉三维模型的重建对于动脉中狭窄和斑块的定位，评估和诊断以及介入介入手术的辅助导航至关重要。在临床实践中，医生使用一些冠状动脉血管造影角度来捕获动脉图像，因此直接从冠状动脉血管造影图像中直接进行3D重建具有很大的实用价值。但是，由于冠状动脉血管的复杂形状以及缺乏数据集和关键点标记，这是一项非常困难的计算机视觉任务。随着深度学习的兴起，使用深神经网络从医学图像中重建人体器官的3D模型正在做越来越多的工作。我们提出了一种从两种不同的冠状动脉血管造影图像的观点来重建三维冠状动脉模型的对抗和生成方式。借助3D完全监督的学习和2D弱监督的学习方案，我们获得了重建精确的精确度，这些精度超过了最先进的技术。
The reconstruction of three-dimensional models of coronary arteries is of great significance for the localization, evaluation and diagnosis of stenosis and plaque in the arteries, as well as for the assisted navigation of interventional surgery. In the clinical practice, physicians use a few angles of coronary angiography to capture arterial images, so it is of great practical value to perform 3D reconstruction directly from coronary angiography images. However, this is a very difficult computer vision task due to the complex shape of coronary blood vessels, as well as the lack of data set and key point labeling. With the rise of deep learning, more and more work is being done to reconstruct 3D models of human organs from medical images using deep neural networks. We propose an adversarial and generative way to reconstruct three dimensional coronary artery models, from two different views of angiographic images of coronary arteries. With 3D fully supervised learning and 2D weakly supervised learning schemes, we obtained reconstruction accuracies that outperform state-of-art techniques.
