Coronary Plaque Characterization from IVUS Image by Using Artificial Intelligence Techniques

Abstract

With the advance of food culture in modern societies there are more and more people suffer from hypertension and diabetes may result in cardiovascular diseases The main factor for cardiovascular diseases is atherosclerosis which is a slow process and would finally lead to severe symptoms like myocardial infarction or brain stroke due plaque rupture or reduction of blood supply to the heart system or brain Intravascular ultrasound (IVUS) imaging has been a common technique to diagnose atherosclerosis in clinical application Owing to its characteristics of real-time and well penetration into vessel walls IVUS imaging provides more geometrical information and composition of blood vessel walls Inside recent commercial IVUS imaging system the software named as Virtual Histology IVUS (VH-IVUS) imaging utilizes the backscattering radiofrequency ultrasound signals to attain its featured information from frequency domain This technique on one hand is proved to correctly characterize plaque types into calcium fibrous fibro-fatty and necrotic cores and provides a referenced information in clinics However on the other hand cardiologists consider that there is still improvement space on plaque characterization technique Therefore this study demonstrates a method based on artificial intelligence to segment borders of media-adventitia and luminal region as well as locations of calcified tissues The in-vivo image dataset is provided from Show Chwan Memorial Hospital Changhua Taiwan and the number of images is 713 from 18 subjects with atherosclerosis In the dataset each grayscale IVUS image is labeled with classes of MA border lumen and calcium The proposed method utilized the method of convolutional neural network in deep learning technique incorporated with the concept of cascaded network to reduce the occurrence of incorrectly detection on the regions outside plaque burden by connecting triple neural networks Besides in the course of learning process three loss functions with different mathematical properties are used to adapt the weighting parameters between neurons in convolutional networks The evaluation measurement is implemented with Dice score precision recall and specificity to estimate the performance of the proposed method In the duration of learning procedure the region of interest is focused on accurate detection of the plaque regional size and calcified tissues since the stabilization or vulnerability of plaque burden is relevant to plaque region and location of calcium From the experiments the method proposed in this study could reach the high accuracy over 0 9 with the usage of various loss functions Although the accuracy in detection of calcium is located at about 0 67 it could provide twice better accurate information in contrast to the results from VH-IVUS These results could provide better characterized information for latter treatment strategies of atherosclerosis

Date of Award	2020
Original language	English
Supervisor	Chih-Chung Huang (Supervisor)