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Automated Detection Framework of the Calcified Plaque with Acoustic Shadowing in IVUS Images

机译：IVUS图像中带有声影阴影的钙化斑块的自动检测框架

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摘要

Intravascular Ultrasound (IVUS) is one ultrasonic imaging technology to acquire vascular cross-sectional images for the visualization of the inner vessel structure. This technique has been widely used for the diagnosis and treatment of coronary artery diseases. The detection of the calcified plaque with acoustic shadowing in IVUS images plays a vital role in the quantitative analysis of atheromatous plaques. The conventional method of the calcium detection is manual drawing by the doctors. However, it is very time-consuming, and with high inter-observer and intra-observer variability between different doctors. Therefore, the computer-aided detection of the calcified plaque is highly desired. In this paper, an automated method is proposed to detect the calcified plaque with acoustic shadowing in IVUS images by the Rayleigh mixture model, the Markov random field, the graph searching method and the prior knowledge about the calcified plaque. The performance of our method was evaluated over 996 in-vivo IVUS images acquired from eight patients, and the detected calcified plaques are compared with manually detected calcified plaques by one cardiology doctor. The experimental results are quantitatively analyzed separately by three evaluation methods, the test of the sensitivity and specificity, the linear regression and the Bland-Altman analysis. The first method is used to evaluate the ability to distinguish between IVUS images with and without the calcified plaque, and the latter two methods can respectively measure the correlation and the agreement between our results and manual drawing results for locating the calcified plaque in the IVUS image. High sensitivity (94.68%) and specificity (95.82%), good correlation and agreement (>96.82% results fall within the 95% confidence interval in the Student t-test) demonstrate the effectiveness of the proposed method in the detection of the calcified plaque with acoustic shadowing in IVUS images.
机译：血管内超声（IVUS）是一种超声成像技术，可获取血管横截面图像以可视化内部血管结构。该技术已广泛用于冠状动脉疾病的诊断和治疗。 IVUS图像中带有声影阴影的钙化斑块的检测在动脉粥样斑块的定量分析中起着至关重要的作用。钙检测的常规方法是由医生手动绘制。但是，这非常耗时，而且不同医生之间的观察者之间和观察者内部差异很大。因此，非常需要计算机辅助检测钙化斑块。本文提出了一种自动方法，通过瑞利混合模型，马尔可夫随机场，图搜索方法和钙化斑块的先验知识，在IVUS图像中检测出带有声影的钙化斑块。我们对从八名患者获得的996张体内IVUS图像进行了评估，评估了我们方法的性能，并将检测到的钙化斑块与一位心脏病医生手动检测到的钙化斑块进行了比较。通过三种评估方法分别对实验结果进行定量分析，即敏感性和特异性测试，线性回归和Bland-Altman分析。第一种方法用于评估区分有或没有钙化斑块的IVUS图像的能力，后两种方法可以分别测量我们的结果与手动绘制结果之间的相关性和一致性，以在IVUS图像中定位钙化斑块。高灵敏度（94.68％）和特异性（95.82％），良好的相关性和一致性（> 96.82％的结果落在Student t检验的95％置信区间内）证明了该方法在检测钙化斑块中的有效性IVUS图像中的声音阴影。

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