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  • Open Access

    ABSTRACT

    Convolution Neural Networks and Support Vector Machines for Automatic Segmentation of Intracoronary Optical Coherence Tomography

    Caining Zhang1, Huaguang Li2, Xiaoya Guo3, David Molony4, Xiaopeng Guo2, Habib Samady4, Don P. Giddens4,5, Lambros Athanasiou6, Rencan Nie2,*, Jinde Cao3,*, Dalin Tang1,*,7

    Molecular & Cellular Biomechanics, Vol.16, Suppl.2, pp. 31-31, 2019, DOI:10.32604/mcb.2019.06983

    Abstract Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques. Optical coherence tomography (OCT) is a recently developed intravascular imaging technique with high resolution approximately 10 microns and could provide accurate quantification of coronary plaque morphology. However, tissue segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective. To overcome these limitations, two automatic segmentation methods for intracoronary OCT image based on support vector machine (SVM) and convolutional neural network (CNN) were performed to identify the plaque region and characterize plaque components. In vivo IVUS and OCT… More >

  • Open Access

    ABSTRACT

    Automatic Segmentation Methods Based on Machine Learning for Intracoronary Optical Coherence Tomography Image

    Caining Zhang1, Xiaoya Guo2, Dalin Tang1,3,*, David Molony4, Chun Yang3, Habib Samady4, Jie Zheng5, Gary S. Mintz6, Akiko Maehara6, Mitsuaki Matsumura6, Don P. Giddens4,7

    Molecular & Cellular Biomechanics, Vol.16, Suppl.1, pp. 79-80, 2019, DOI:10.32604/mcb.2019.05747

    Abstract Cardiovascular diseases are closely associated with sudden rupture of atherosclerotic plaques. Previous image modalities such as magnetic resonance imaging (MRI) and intravascular ultrasound (IVUS) were unable to identify vulnerable plaques due to their limited resolution. Optical coherence tomography (OCT) is an advanced intravascular imaging technique developed in recent years which has high resolution approximately 10 microns and could provide more accurate morphology of coronary plaque. In particular, it is now possible to identify plaques with fibrous cap thickness <65 μm, an accepted threshold value for vulnerable plaques. However, the current segmentation of OCT images are… More >

  • Open Access

    ARTICLE

    Convolution Neural Networks and Support Vector Machines for Automatic Segmentation of Intracoronary Optical Coherence Tomography

    Caining Zhang1, Huaguang Li2, Xiaoya Guo3, David Molony4, Xiaopeng Guo2, Habib Samady4, Don P. Giddens4,5, Lambros Athanasiou6, Rencan Nie2,*, Jinde Cao3,*, Dalin Tang1,*,7

    Molecular & Cellular Biomechanics, Vol.16, No.2, pp. 153-161, 2019, DOI:10.32604/mcb.2019.06873

    Abstract Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques. Optical coherence tomography (OCT) is a recently developed intravascular imaging technique with high resolution approximately 10 microns and could provide accurate quantification of coronary plaque morphology. However, tissue segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective. To overcome these limitations, two automatic segmentation methods for intracoronary OCT image based on support vector machine (SVM) and convolutional neural network (CNN) were performed to identify the plaque region and characterize plaque components. In vivo IVUS and OCT coronary… More >

  • Open Access

    ARTICLE

    Stress-Based Plaque Vulnerability Index and Assessment for Carotid Atherosclerotic Plaques Using Patient-Specific Vessel Material Properties

    Qingyu Wang1, Dalin Tang1,2,*, Gador Canton3, Zheyang Wu2, Thomas S. Hatsukami4, Kristen L. Billiar5, Chun Yuan6

    Molecular & Cellular Biomechanics, Vol.15, No.4, pp. 189-201, 2018, DOI:10.32604/mcb.2018.04572

    Abstract Cardiovascular diseases are closely linked to atherosclerotic plaque development and rupture. Assessment of plaque vulnerability is of fundamental significance to cardiovascular research and disease diagnosis, prevention, treatment and management. Magnetic resonance image (MRI) data of carotid atherosclerotic plaques from 8 patients (5 male, 3 female; age: 62-83, mean=71) were acquired at the University of Washington (UW), Seattle by the Vascular Imaging Laboratory (VIL) with written informed consent obtained. Patient-specific vessel material properties were quantified using Cine MRI data for modeling use. 3D thin-layer models were used to obtain plaque stress and strain for plaque assessment.… More >

  • Open Access

    ARTICLE

    Patient-Specific Artery Shrinkage and 3D Zero-Stress State in Multi-Component 3D FSI Models for Carotid Atherosclerotic Plaques Based on In Vivo MRI Data

    Xueying Huang*, Chun Yang, Chun Yuan, Fei Liu, Gador Canton, Jie Zheng§, Pamela K. Woodard§, Gregorio A. Sicard, Dalin Tang||

    Molecular & Cellular Biomechanics, Vol.6, No.2, pp. 121-134, 2009, DOI:10.3970/mcb.2009.006.121

    Abstract Image-based computational models for atherosclerotic plaques have been developed to perform mechanical analysis to quantify critical flow and stress/strain conditions related to plaque rupture which often leads directly to heart attack or stroke. An important modeling issue is how to determine zero stress state from in vivo plaque geometries. This paper presents a method to quantify human carotid artery axial and inner circumferential shrinkages by using patient-specific ex vivo and in vivo MRI images. A shrink-stretch process based on patient-specific in vivo plaque morphology and shrinkage data was introduced to shrink the in vivo geometry first to find the zero-stress… More >

  • Open Access

    ARTICLE

    In Vivo/Ex Vivo MRI-Based 3D Non-Newtonian FSI Models for Human Atherosclerotic Plaques Compared with Fluid/Wall-Only Models

    Chun Yang1, Dalin Tang2, Chun Yuan3, Thomas S. Hatsukami4, Jie Zheng5, Pamela K. Woodard5

    CMES-Computer Modeling in Engineering & Sciences, Vol.19, No.3, pp. 233-246, 2007, DOI:10.3970/cmes.2007.019.233

    Abstract It has been recognized that fluid-structure interactions (FSI) play an important role in cardiovascular disease initiation and development. However, in vivo MRI multi-component FSI models for human carotid atherosclerotic plaques with bifurcation and quantitative comparisons of FSI models with fluid-only or structure-only models are currently lacking in the literature. A 3D non-Newtonian multi-component FSI model based on in vivo/ex vivo MRI images for human atherosclerotic plaques was introduced to investigate flow and plaque stress/strain behaviors which may be related to plaque progression and rupture. Both artery wall and plaque components were assumed to be hyperelastic,… More >

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