Automatic classification of atherosclerotic plaques imaged with intravascular OCT

Abstract

Intravascular optical coherence tomography (IV-OCT) allows evaluation of atherosclerotic plaques; however, plaque characterization is performed by visual assessment and requires a trained expert for interpretation of the large data sets. Here, we present a novel computational method for automated IV-OCT plaque characterization. This method is based on the modeling of each A-line of an IV-OCT data set as a linear combination of a number of depth profiles. After estimating these depth profiles by means of an alternating least square optimization strategy, they are automatically classified to predefined tissue types based on their morphological characteristics. The performance of our proposed method was evaluated with IV-OCT scans of cadaveric human coronary arteries and corresponding tissue histopathology. Our results suggest that this methodology allows automated identification of fibrotic and lipid-containing plaques. Moreover, this novel computational method has the potential to enable high throughput atherosclerotic plaque characterization.

Tissue characterization with depth-resolved attenuation coefficient and backscatter term in intravascular optical coherence tomography images

“…Given the large difference in attenuation to the other types of tissues, the impact caused by the imaging resolution in detecting the presence of macrophages is expected to be small. For the sake of real-time computing, a median filter is applied rather than a more complex speckle noise removal filter such as the iwhTV denoising published recently 34 or the entropy filter used by Jimenez et al 45 A fixed window size has been selected as around 60 μm. As it was discussed, the median filter blurs only the attenuation at the border of different tissues, and it affects the local statistical numbers minimally.…”

Coronary calcification segmentation in intravascular OCT images using deep learning: application to calcification scoring

“…Our group developed machine learning 21 and deep learning 22,23 methods to automatically classify plaque regions. Rico-Jimenez et al 24 used linear discriminant analysis to identify normal and fibrolipidic A-lines. Yong et al 25 proposed a linear regression convolutional neural network to automatically segment the vessel lumen.…”

Characterization of coronary artery pathological formations from OCT imaging using deep learning

“…Automated tissue analysis and plaque detection were focused on 2D intracoronary OCT images in adult patients to visualize plaque formations [19][20][21][22][23][24][25]. Combination of light backscattering and attenuation coefficients have been estimated from intracoronary time domain OCT for three different atherosclerosis tissues, namely calcification, lipid pool, and fibrosis [19].…”

“…Combination of texture features and optical properties of tissues is used to train a relevance vector machine (RVM) to perform the classification task [24]. A plaque tissue characterization technique based on intrinsic morphological characteristics of the A-lines using OCT imaging is proposed to classify superficial-lipid, fibrotic-lipid, fibrosis, and intimal thickening by applying Linear Discriminant Analysis (LDA) [25].…”