Purpose

Vulnerable plaque detection is important to acute coronary syndrome (ACS) diagnosis. In recent years, intravascular optical coherence tomography (IVOCT) imaging has been used for vulnerable plaque detection. Current automated detection methods adopt the traditional image classification and object detection algorithms, such as the logistic regression model, SVM, and Haar-Adaboost, to detect vulnerable plaques. The detection quality of these methods is relatively low. The aim of this study is to improve the detection quality of vulnerable plaque.

Methods

We propose an automatic detection system of vulnerable plaque for IVOCT images based on deep convolutional neural network (DCNN). The system is mainly composed of four modules: pre-processing, deep convolutional neural networks (DCNNs), post-processing, and ensemble. The IVOCT images input to DCNNs are firstly pre-processed by using the methods of de-noising and data augmentation. Then multiple DCNNs are used to detect the vulnerable plaques in the IVOCT images; the vulnerable plaque regions and their corresponding labels and scores are output. Next, the output results of each network are processed by the post-processing module. We propose three algorithms, union of intersecting regions, duplicated region processing, and small gaps removal for post-processing. Finally, the output detection results of multiple networks are combined using a proposed combining method in ensemble module.

Results

We evaluated the proposed method in a dataset of 300 IVOCT images. Experimental results show that our system can achieve a precision rate of 88.84%, a recall rate of 95.02%, and an overlap rate of 85.09%; the detection quality score is 88.46%.

Conclusions

The proposed algorithms can detect vulnerable plaques with superior performance; our system can be used as an auxiliary diagnostic tool for vulnerable plaque detection in IVOCT images.

中文翻译：

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血管内光学相干断层扫描图像的易损斑块的自动检测。

目的

易损斑块检测对急性冠脉综合征（ACS）诊断很重要。近年来，血管内光学相干断层扫描（IVOCT）成像已用于易损斑块检测。当前的自动检测方法采用传统的图像分类和对象检测算法（例如逻辑回归模型，SVM和Haar-Adaboost）来检测易损斑块。这些方法的检测质量相对较低。这项研究的目的是提高易损斑块的检测质量。

方法

我们提出了一种基于深度卷积神经网络（DCNN）的IVOCT图像脆弱斑块自动检测系统。该系统主要由四个模块组成：预处理，深度卷积神经网络（DCNN），后处理和集成。首先使用降噪和数据增强方法对输入到DCNN的IVOCT图像进行预处理。然后使用多个DCNN来检测IVOCT图像中的易损斑块。输出易损斑块区域及其相应的标签和分数。接下来，由后处理模块处理每个网络的输出结果。我们提出了三种算法：相交区域的并集，重复区域处理和用于后处理的小间隙消除。最后，

结果

我们在300个IVOCT图像的数据集中评估了该方法。实验结果表明，该系统可以达到88.84％的查准率，95.02％的查全率，85.09％的重叠率。检测质量得分为88.46％。

结论

所提出的算法可以检测出性能卓越的易损斑块。我们的系统可以用作IVOCT图像中易损斑块检测的辅助诊断工具。