Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151550
Title: Automatic plaque segmentation in coronary optical coherence tomography images
Authors: Zhang, Huaqi
Wang, Guanglei
Li, Yan
Lin, Feng
Han, Yechen
Wang, Hongrui
Keywords: Engineering::Computer science and engineering
Science::Medicine
Issue Date: 2019
Source: Zhang, H., Wang, G., Li, Y., Lin, F., Han, Y. & Wang, H. (2019). Automatic plaque segmentation in coronary optical coherence tomography images. International Journal of Pattern Recognition and Artificial Intelligence, 33(14), 1954035-. https://dx.doi.org/10.1142/S0218001419540351
Journal: International Journal of Pattern Recognition and Artificial Intelligence
Abstract: Coronary optical coherence tomography (OCT) is a new high-resolution intravascular imaging technology that clearly depicts coronary artery stenosis and plaque information. Study of coronary OCT images is of significance in the diagnosis of coronary atherosclerotic heart disease (CAD). We introduce a new method based on the convolutional neural network (CNN) and an improved random walk (RW) algorithm for the recognition and segmentation of calcified, lipid and fibrotic plaque in coronary OCT images. First, we design CNN with three different depths (2, 4 or 6 convolutional layers) to perform the automatic recognition and select the optimal CNN model. Then, we device an improved RW algorithm. According to the gray-level distribution characteristics of coronary OCT images, the weights of intensity and texture term in the weight function of RW algorithm are adjusted by an adaptive weight. Finally, we apply mathematical morphology in combination with two RWs to accurately segment the plaque area. Compared with the ground truth of clinical segmentation results, the Jaccard similarity coefficient (JSC) of calcified and lipid plaque segmentation results is 0.864, the average symmetric contour distance (ASCD) is 0.375mm, the JSC and ASCD reliabilities are 88.33% and 92.50% respectively. The JSC of fibrotic plaque is 0.876, the ASCD is 0.349mm, the JSC and ASCD reliabilities are 90.83% and 95.83% respectively. In addition, the average segmentation time (AST) does not exceed 5 s. Reliable and significantly improved results have been achieved in this study. Compared with the CNN, traditional RW algorithm and other methods. The proposed method has the advantages of fast segmentation, high accuracy and reliability, and holds promise as an aid to doctors in the diagnosis of CAD.
URI: https://hdl.handle.net/10356/151550
ISSN: 0218-0014
DOI: 10.1142/S0218001419540351
Rights: © 2019 World Scientific Publishing Company. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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