Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
Comput Med Imaging Graph. 2021 Apr;89:101900. doi: 10.1016/j.compmedimag.2021.101900. Epub 2021 Mar 11.
The automatic detection of coronary artery stenosis on X-ray images is important in coronary heart disease diagnosis. Conventional methods cannot accurately detect all stenosis areas because of heartbeat, respiratory movements and weak vascular features in single-frame contrast images.
This paper proposes the use of Stenosis-DetNet, which is a method based on object detection networks. A sequence feature fusion module and a sequence consistency alignment module are designed to maximize temporal information to achieve accurate detection results. The sequence feature fusion module fuses all candidate box features and uses the temporal information to enhance these features. The sequence consistency alignment module optimizes the initial results by using the coronary artery displacement information and image features of the adjacent images and leads to the final detection of coronary artery stenosis.
In the experiment, 166 X-ray image sequences were used for training and testing. Compared with the three existing stenosis detection methods, the precision and sensitivity of Stensis-DetNet were 94.87 % and 82.22 %, respectively, which were better than those of the other three methods.
Our proposed method effectively suppressed the false positive and false negative results of stenosis detection in sequence angiography images. It was superior to the state-of-art methods.
在 X 射线图像上自动检测冠状动脉狭窄对于冠心病的诊断很重要。由于单帧对比度图像中的心跳、呼吸运动和血管特征较弱，传统方法无法准确检测到所有狭窄区域。
本文提出使用 Stenosis-DetNet，这是一种基于目标检测网络的方法。设计了序列特征融合模块和序列一致性对齐模块，以最大化时间信息，从而获得准确的检测结果。序列特征融合模块融合所有候选框特征，并利用时间信息增强这些特征。序列一致性对齐模块通过使用冠状动脉位移信息和相邻图像的图像特征来优化初始结果，从而实现冠状动脉狭窄的最终检测。
在实验中，使用了 166 个 X 射线图像序列进行训练和测试。与现有的三种狭窄检测方法相比，Stensis-DetNet 的精度和灵敏度分别为 94.87%和 82.22%，优于其他三种方法。
我们提出的方法有效地抑制了序列血管造影图像中狭窄检测的假阳性和假阴性结果。它优于现有的方法。
