Automatic classification of atherosclerotic tissue in intravascular optical coherence tomography images

Abstract

Intravascular optical coherence tomography (IVOCT) has been successfully utilized for in vivo diagnostics of coronary plaques. However, classification of atherosclerotic tissues is mainly performed manually by experienced experts, which is time-consuming and subjective. To overcome these limitations, an automatic method of segmentation and classification of IVOCT images is developed in this paper. The method is capable of detecting the plaque contour between the fibrous tissues and other components. Subsequently, the method classifies the tissues based on their texture features described by Fourier transform and discrete wavelet transform. The experimental results of 103 images show that an overall classification accuracy of over 80% in the indicator of depth and span angle is achieved in comparison to manual results. The validation suggests that this method is objective, accurate, and automatic without any manual intervention. The proposed method is able to demonstrate the artery wall morphology successfully, which is valuable for the research of atherosclerotic disease.

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

“…Athanasiou et al 19 segmented calcification and then classified lipid, fibrous, and mixed tissues using 17 features with k-means and postanalysis. Zhou et al 20 developed a classification and segmentation method using texture features described by the Fourier transform and discrete wavelet transform to classify adventitia, calcification, lipid, and mixed tissue. Our group developed machine learning 21 and deep learning 22,23 methods to automatically classify plaque regions.…”

Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets

“…26 Athanasiou et al 27 utilized intensity and texture features to identify calcium, lipid, fibrous, and mixed tissues using k-means clustering and random forest classifier. Zhou et al 28 used edge detection to identify adventitia and calcium tissue, and they further refined the calcium boundary using a level-set approach. In addition, they used wavelet and intensity features with a random forest classifier to find lipid and mixed tissues.…”

“…First, and most important, we evaluated an A-line classification algorithm that uses the most comprehensive set of handcrafted features (∼5000) to date. We included many features previously used for IVOCT plaque analysis 23,[26][27][28] and introduced innovative types, including vessel-lumen morphology, which evaluates the irregular lumen shape often present in calcifications, and three-dimensional (3-D) digital edge and texture, which can encompass information from nearby IVOCT image frames. Second, we created two large, labeled in-vivo and ex-vivo datasets (∼60 pullbacks, ∼7000 image frames), which include a very wide variety of lesions, giving an opportunity to better test generalizability of algorithms.…”

A Novel Methodology for Detection of Lumen, Outer Wall, Plaques and Stent Struts in Coronary Arteries Using Optical Coherence Tomography

“…Yellow: detected struts F. Calcified plaques segmentation and non-calcified plaques characterization 3 cluster K-Means is performed on the ROI between the lumen and outer border of adventitia. Then, closed shapes enclosed by the cluster representing Media are detected and hysteresis thresholding is performed to enhance the outer border of the calcific tissue [13]. Finally, a series of morphological operations to clear out noise and enhance calcifications are performed.…”

“…Zhou et al performed automatic detection of plaques and classification of features based on textural features with accuracy over 80% [6]. A deep learning method was implemented by Gessert et al that detected calcified, fibrous and lipid plaques with an overall accuracy 91.7%, sensitivity 90.9%, specificity 92.4% [7].…”