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  • Open Access


    Machine Learning Model Comparison for Automatic Segmentation of Intracoronary Optical Coherence Tomography and Plaque Cap Thickness Quantification

    Caining Zhang1, Xiaopeng Guo2, Xiaoya Guo3, David Molony4, Huaguang Li2, Habib Samady4, Don P. Giddens4,5, Lambros Athanasiou6, Dalin Tang1*,7, Rencan Nie2,*, Jinde Cao8

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.2, pp. 631-646, 2020, DOI:10.32604/cmes.2020.09718

    Abstract Optical coherence tomography (OCT) is a new intravascular imaging technique with high resolution and could provide accurate morphological infor￾mation for plaques in coronary arteries. However, its segmentation is still com￾monly performed manually by experts which is time-consuming. The aim of this study was to develop automatic techniques to characterize plaque components and quantify plaque cap thickness using 3 machine learning methods including convolutional neural network (CNN) with U-Net architecture, CNN with Fully convolutional DenseNet (FC-DenseNet) architecture and support vector machine (SVM). In vivo OCT and intravascular ultrasound (IVUS) images were acquired from two patients at Emory University with informed consent… More >

  • Open Access


    The Correlation Between Texture Features and Fibrous Cap Thickness of Lipid-Rich Atheroma Based on Optical Coherence Tomography Imaging

    Chunliu He1, Jiaqiu Wang2, Yuxiang Huang1, Tongjing Zhu1, Yuehong Miao1, Zhiyong Li1,2*

    Molecular & Cellular Biomechanics, Vol.13, No.1, pp. 23-36, 2016, DOI:10.3970/mcb.2016.013.027

    Abstract Fibrous cap thickness (FCT) is seen as critical to plaque vulnerability. Therefore, the development of automatic algorithms for the quantification of FCT is for estimating cardiovascular risk of patients. Intravascular optical coherence tomography (IVOCT) is currently the only in vivo imaging modality with which FCT, the critical component of plaque vulnerability, can be assessed accurately. This study was aimed to discussion the correlation between the texture features of OCT images and the FCT in lipid-rich atheroma. Methods: Firstly, a full automatic segmentation algorithm based on unsupervised fuzzy c means (FCM) clustering with geometric constrains was developed to segment the ROIs… More >

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