狭窄
特征(语言学)
人工智能
一致性(知识库)
计算机视觉
医学
序列(生物学)
放射科
计算机科学
冠状动脉疾病
血管造影
模式识别(心理学)
心脏病学
哲学
生物
遗传学
语言学
作者
Kun Pang,Danni Ai,Huihui Fang,Jingfan Fan,Hong Song,Jian Yang
标识
DOI:10.1016/j.compmedimag.2021.101900
摘要
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.
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