Caining Zhang¹, Xiaopeng Guo², Xiaoya Guo³, David Molony⁴, Huaguang Li², Habib Samady⁴, Don P. Giddens^4,5, Lambros Athanasiou⁶, Dalin Tang^1*,7, Rencan Nie^2,*, 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 >

Chunliu He¹, Jiaqiu Wang², Yuxiang Huang¹, Tongjing Zhu¹, Yuehong Miao¹, Zhiyong Li^1,2*

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 >