Calcification detection of coronary artery disease in intravascular ultrasound image: Deep feature learning approach

Coronary artery disease (CAD) is part of the non-communicable disease (NCD) in cardiovascular disease (CVD). The blood vessel area became narrow when the calcification with the plaque embedded in the coronary artery inner wall. The radiologists and medical practitioners used visual inspection to detect calcification on IVUS image. The presence of calcification will not be able to do the measurement to calculate the maximum diameter and the maximum area for the patient coronary artery either before treatment or after treatment. More than 100 frames per patient is needed to analyse the location of the calcification. In this study, our aim is to detect the presence and the absence of the calcification in the coronary artery using intravascular ultrasound (IVUS) images with catheter frequency of 20MHz. The IVUS images used were the original Cartesian coordinate image and the polar reconstructed coordinate image. In this study, three types of convolutional neural network (CNN) using Directed Acyclic Graph networks, were used together with five types of classifiers. The dataset used to demonstrate our framework is Dataset B from MICCAI Challenge 2011 that consists of 2175 coronary artery disease IVUS image where 530 are IVUS images with calcification and 1645 are IVUS images without calcification. The cross validation for testing and training, the k-fold value used was 2, 3, 5 and 10. The performance measures for the ResNet-50, the ResNet-101 and the Inception-V3 model shows an excellent result using support vector machine classifier and discriminant analysis for both types of images. A better improvement using polar reconstructed coordinate image when using decision tree classifier and Naïve Bayes classifier whilst ResNet-101 architecture shows an excellent performance measure when applying images polar reconstructed images when using k-nearest neighbor classifier. However, Naïve Bayes classifier has an excellent result when using Inception-V3 architecture

Iterative self-organizing atherosclerotic tissue labeling in intravascular ultrasound images and comparison with virtual histology.

Intravascular ultrasound (IVUS) is the predominant imaging modality in the field of interventional cardiology that provides real-time cross-sectional images of coronary arteries and the extent of atherosclerosis. Due to heterogeneity of lesions and stringent spatial/spectral behavior of tissues, atherosclerotic plaque characterization has always been a challenge and still is an open problem. In this paper, we present a systematic framework from in vitro data collection, histology preparation, IVUS-histology registration along with matching procedure, and finally a robust texture-derived unsupervised atherosclerotic plaque labeling. We have performed our algorithm on in vitro and in vivo images acquired with single-element 40 MHz and 64-elements phased array 20 MHz transducers, respectively. In former case, we have quantified results by local contrasting of constructed tissue colormaps with corresponding histology images employing an independent expert and in the latter case, virtual histology images have been utilized for comparison. We tackle one of the main challenges in the field that is the reliability of tissues behind arc of calcified plaques and validate the results through a novel random walks framework by incorporating underlying physics of ultrasound imaging. We conclude that proposed framework is a formidable approach for retrieving imperative information regarding tissues and building a reliable training dataset for supervised classification and its extension for in vivo applications