Automated detection of vulnerable plaque in intravascular ultrasound images.
Acute coronary syndrome (ACS) is a syndrome caused by a decrease in blood flow in the coronary arteries. The ACS is usually related to coronary thrombosis and is primarily caused by plaque rupture followed by plaque erosion and calcified nodule. Thin-cap fibroatheroma (TCFA) is known to be the most...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 4; pp. 863 - 877 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135753235&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135753235 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2019 vid: 57 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135753235 135753235 NLM30426362 10.1007/s11517-018-1925-x NLM30426362 135753235 ppf: 863 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated detection of vulnerable plaque in intravascular ultrasound images. aug: au: Jun, Tae Joon Kang, Soo-Jin Lee, June-Goo Kweon, Jihoon Na, Wonjun Kang, Daeyoun Kim, Dohyeun Kim, Daeyoung Kim, Young-Hak affil: School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea sug: subj: Ultrasonography Image Processing, Computer Assisted Atherosclerosis Diagnosis Atherosclerosis Algorithms Pharmacokinetics Neural Networks (Computer) Tomography, Optical Coherence Automation Reproducibility of Results Questionnaires ab: Acute coronary syndrome (ACS) is a syndrome caused by a decrease in blood flow in the coronary arteries. The ACS is usually related to coronary thrombosis and is primarily caused by plaque rupture followed by plaque erosion and calcified nodule. Thin-cap fibroatheroma (TCFA) is known to be the most similar lesion morphologically to a plaque rupture. In this paper, we propose methods to classify TCFA using various machine learning classifiers including feed-forward neural network (FNN), K-nearest neighbor (KNN), random forest (RF), and convolutional neural network (CNN) to figure out a classifier that shows optimal TCFA classification accuracy. In addition, we suggest pixel range-based feature extraction method to extract the ratio of pixels in the different region of interests to reflect the physician's TCFA discrimination criteria. Our feature extraction method examines the pixel distribution of the intravascular ultrasound (IVUS) image at a given ROI, which allows us to extract general characteristics of the IVUS image while simultaneously reflecting the different properties of the vessel's substances such as necrotic core and calcified nodule depending on the brightness of the pixel. A total of 12,325 IVUS images were labeled with corresponding optical coherence tomography (OCT) images to train and evaluate the classifiers. We achieved 0.859, 0.848, 0.844, and 0.911 area under the ROC curve (AUC) in the order of using FNN, KNN, RF, and CNN classifiers. As a result, the CNN classifier performed best and the top 10 features of the feature-based classifiers (FNN, KNN, RF) were found to be similar to the physician's TCFA diagnostic criteria. Graphical Abstract AUC result of proposed classifiers. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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