An artificial neural network method for lumen and media-adventitia border detection in IVUS
An artificial neural network method for lumen and media-adventitia border detection in IVUS
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DOI：
10.1016/j.compmedimag.2016.11.003
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发表时间：
2017-04-01
影响因子：
5.7
通讯作者：
Zhang, Heye
中科院分区：
文献类型：
--
作者：
Su, Shengran;Hu, Zhenghui;Zhang, Heye
血管内超声(IVUS)已被公认为是评价冠状动脉内狭窄的一种强有力的成像技术。IVUS图像中管腔边界和中膜-外膜(MA)边界的检测是确定冠状动脉内斑块负荷的关键步骤，但由于IVUS图像的数据量大，这一检测可能会给医生带来繁重的负担。本文采用人工神经网络(ANN)方法作为特征学习算法，对IVUS图像中的管腔和MA边界进行检测。该方法以空间、相邻两类图像信息作为ANN方法的输入数据，通过两个稀疏的自动编码器和一个Softmax分类器对不同的血管层进行相应的区分。用另一个人工神经网络对第一个网络的结果进行优化。最后，利用活动轮廓模型对人工神经网络检测到的管腔和MA边界进行平滑处理。将我们的方法与两位IVUS专家在四个受试者的461幅IVUS图像上进行的手工绘制方法进行了比较。结果表明，该方法与手工作图结果具有较高的相关性和较好的一致性。人工神经网络方法的检测误差接近两组手工作图结果之间的误差。所有这些结果表明，我们提出的方法可以有效和准确地检测IVUS图像中的管腔和MA边界。(C)2016爱思唯尔有限公司。保留所有权利。
Intravascular ultrasound (IVUS) has been well recognized as one powerful imaging technique to evaluate the stenosis inside the coronary arteries. The detection of lumen border and media-adventitia (MA) border in IVUS images is the key procedure to determine the plaque burden inside the coronary arteries, but this detection could be burdensome to the doctor because of large volume of the IVUS images. In this paper, we use the artificial neural network (ANN) method as the feature learning algorithm for the detection of the lumen and MA borders in IVUS images. Two types of imaging information including spatial, neighboring features were used as the input data to the ANN method, and then the different vascular layers were distinguished accordingly through two sparse auto-encoders and one softmax classifier. Another ANN was used to optimize the result of the first network. In the end, the active contour model was applied to smooth the lumen and MA borders detected by the ANN method. The performance of our approach was compared with the manual drawing method performed by two IVUS experts on 461 IVUS images from four subjects. Results showed that our approach had a high correlation and good agreement with the manual drawing results. The detection error of the ANN method close to the error between two groups of manual drawing result. All these results indicated that our proposed approach could efficiently and accurately handle the detection of lumen and MA borders in the IVUS images. (C) 2016 Elsevier Ltd. All rights reserved.