Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images

Abstract

Intravascular optical coherence tomography (IVOCT) is rapidly becoming the method of choice for the in vivo investigation of coronary artery disease. While IVOCT visualizes atherosclerotic plaques with a resolution <20µm, image analysis in terms of tissue composition is currently performed by a time-consuming manual procedure based on the qualitative interpretation of image features. We illustrate an algorithm for the automated and systematic characterization of IVOCT atherosclerotic tissue. The proposed method consists in a supervised classification of image pixels according to textural features combined with the estimated value of the optical attenuation coefficient. IVOCT images of 64 plaques, from 49 in vivo IVOCT data sets, constituted the algorithm’s training and testing data sets. Validation was obtained by comparing automated analysis results to the manual assessment of atherosclerotic plaques. An overall pixel-wise accuracy of 81.5% with a classification feasibility of 76.5% and per-class accuracy of 89.5%, 72.1% and 79.5% for fibrotic, calcified and lipid-rich tissue respectively, was found. Moreover, measured optical properties were in agreement with previous results reported in literature. As such, an algorithm for automated tissue characterization was developed and validated using in vivo human data, suggesting that it can be applied to clinical IVOCT data. This might be an important step towards the integration of IVOCT in cardiovascular research and routine clinical practice.

Wavelet Analysis for Multiresolution Tissue Characterization in Intracoronary Optical Images

“…That method, achieved 76% of Dice metric, which is lower than this current solution. Ughi et al [6] developed an automated tissue characterization based on texture analysis, attenuation coefficient and pixel classification using Random Forest, reaching an accuracy of 89.5% for fibrotic tissue, which is lower than our method (99.6%). Athanasiou et al [10] developed an automated method based on segmentation and classification using K-means and achieved 87% and 0.09mm 2 for sensitivity and MADA respectively, better than our solution.…”

“…An advantage of the proposed wavelet method over works [6,10] is that it does not require a training stage, and we believe our results can be improved with a parameters tuning for scalogram binarization. Moreover, the multiresolution aspect of wavelets can be also used for lipid and calcium plaque characterization.…”

“…However changes may occur in brightness according to the position of the catheter at the time of frame acquisition. Methods based only on texture analysis [6] and intensity of the pixels may be adversely affected by changes in light intensity caused by the catheter movement. Thus, frequency domain-based methods can overcome these image intensity problems.…”

Automated segmentation and characterization of esophageal wall in vivo by tethered capsule optical coherence tomography endomicroscopy

“…In this situation, the proposed algorithm misclassified folded squamous esophagus, such as those seen around areas of lack of contact, as BE. In order to address this issue, the proposed framework may be further expanded including tissue backscattering quantification (e.g., by the means of texture analysis such as co-occurrence matrices [18] and wavelet analysis), however this would significantly increase algorithm complexity and processing time. Since these artifacts usually manifest as longitudinal stripes along the esophagus, it is also possible to remove them computationally using shape analysis image processing techniques and ignoring areas in the proximity of regions that demonstrate lack of contact.…”

Automatic classification of atherosclerotic plaques imaged with intravascular OCT

“…Yet, it is prone to erroneously compute the backscatter coefficient due tissue heterogeneity and the lack of an intensity calibration. Similarly, estimation of the optical attenuation coefficient combined with a supervised classification of image pixels according to textural features was proposed to improve the automated and systematic characterization with IV-OCT of the following atherosclerotic plaque types: fibrotic, calcified, and lipid-rich; yet, this procedure requires user input for the selection of regions of interest (ROIs) for plaque assessment [14]. Recently, a method was proposed to estimate the attenuation coefficient as a function of depth for each A-line in order to to identify calcifications, necrotic core, and mixed plaque [15].…”