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

Giovanni Jacopo Ughi; Tom Adriaenssens; Peter Sinnaeve; Walter Desmet; Jan D’hooge

doi:10.1364/BOE.4.001014

Biomedical Optics Express
Vol. 4,
Issue 7,
pp. 1014-1030
(2013)
•https://doi.org/10.1364/BOE.4.001014

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

Giovanni Jacopo Ughi, Tom Adriaenssens, Peter Sinnaeve, Walter Desmet, and Jan D’hooge

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Giovanni Jacopo Ughi, Tom Adriaenssens, Peter Sinnaeve, Walter Desmet, and Jan D’hooge, "Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images," Biomed. Opt. Express 4, 1014-1030 (2013)

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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.

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Fig. 1 Examples of atherosclerotic plaques. Left image shows a lipid-rich plaque (L); right image a circumferential calcification (C). Asterisk indicates guide-wire shadowing artifact.

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Fig. 2 Flowchart of the image processing algorithm for tissue characterization. Output consists of a tissue image map. Intermediate output is given by the attenuation image-map.

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Fig. 3 Illustration of the fully-automated lumen segmentation procedure. Image (a) shows the polar domain image. Image (b) illustrates the application of the Otsu’s method and morphological operations and image (c) results of the area constrain. Image (d) and (e) show segmentation results before and after scan-conversion respectively. On image (a) the arrow is pointing to the OCT imaging catheter and the asterisk indicates the guide-wire shadowing artifact.

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Fig. 4 A schematic representation of the iterative fitting procedure. From this image it is possible to appreciate how the algorithm tests all the possible solutions for every value of k varying П(di).

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Fig. 5 Attenuation imaging examples. Image (a) contains a lipid-rich plaque (L) and peri-vascular tissue (PV). Image (b) contains a mixed atherosclerotic plaque containing both lipid-rich (L) and calcific (C) tissue; image (c) shows an example of intimal thickening (fibrotic tissue). Automatic analysis correctly depicts lipid-rich tissue with a higher attenuation coefficient. Attenuation images µ_t(d) are displayed on a color scale from 0 (dark blue) to 12 mm⁻¹ (dark red). Asterisks indicate the guide-wire shadowing artifact.

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Fig. 6 Classification examples. Image (a) shows an example of lipid-rich tissue (L); image (b) a calcified plaque (C); image (c) a fibrotic plaque (F); image (d) a mixed plaque containing both calcific and lipid-rich tissues (arrow). From the images it is possible to appreciate automated characterization of the 3 main plaque components and outliers (last row) by comparing to ground truth (manual analysis - middle row). Asterisks indicate guide-wire shadowing artifact and (†) indicates a side-branch. Color coding: yellow = lipid-rich, green = fibrotic, white = calcific and gray = outliers.

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Fig. 7 Analysis of 3 adjacent frames. Automated tissue characterization procedure shows consistency through multiple images. Asterisk indicates guide-wire shadowing artifact while (L) indicates lipid-rich tissue.

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Fig. 8 3D rendering of an entire vessel volume depicting different plaque components. Multiple adjacent IVOCT images are automatically analyzed and further processed with dedicated software (OsiriX [41]). Prior to rendering, results were visually inspected and manually corrected where needed. Images immediately illustrate the 3D distribution of atherosclerotic tissue in the analyzed vessel. The arrow points to a vessel stenosis presenting the IVOCT minimal lumen area. The red asterisk indicates guide-wire shadowing artifact. Color coding: yellow = lipid-rich, white = calcific tissue and orange-to-gray rendering of the vessel wall.

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Tables (3)

Table 1 Attenuation and Backscattering for the Main Atherosclerotic Plaque Components [12]

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Table 2 Definitions for the First and Second Order Statistical Features Used in This Study

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Table 3 Measured Values of Optical Properties and Textural Features for the Different Plaque Types^a

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Equations (6)

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I (d) \propto I_{0} \cdot \exp (- μ_{t} \cdot d)

T (d) = {[{(\frac{d - x_{0}}{z_{0}})}^{2} + 1]}^{- 1 / 2}

I (d) \propto T (d) \cdot I_{0} \cdot \exp (- μ_{t} \cdot d)

I (d) \propto T (d) \cdot \sum_{i = 1}^{k} I_{0, i} \cdot \exp (- μ_{t, i} \cdot d) \cdot \prod (d - d_{i})

\prod (d - d_{i}) = {{_{}^{}}_{0 \begin{matrix} o t h e r w i s e \end{matrix}}^{1 \begin{matrix} d_{i} + t < d < d_{i + 1} \end{matrix}}

I_{0, i} = I_{0, i} (d_{i} + t)

Plaque type	Backscattering	Attenuation
Fibrous	High	Low
Lipid	High+	High
Calcium	Low	Low-

Mean (1st order stat.)	$\frac{1}{N} \sum_{i = 1}^{N} I_{k}$	where N is the number of pixels in a given region and I_k the int. value of a pixel
Co-occurrence probability (2nd order stat.)	$C_{i j} = \frac{P_{i, j}}{\sum_{i, j = 1}^{G} P_{i, j}}$	where P_i,j represents the number of occurrences of gray levels i and j for a certain (d, θ) pair in a given region
Contrast	$\sum C_{i j} {(i - j)}^{2}$	$\sum = \sum_{i = 1}^{G} \sum_{j = 1}^{G}$
Cluster shade	$\sum C_{i j} {(i + j - μ_{i} - μ_{j})}^{3}$	$μ_{i} = \sum i C_{i j}$
Cluster shade	$\sum C_{i j} {(i + j - μ_{i} - μ_{j})}^{3}$	$μ_{j} = \sum j C_{i j}$
Energy	$\sum C_{i j}^{2}$
Entropy*	$- \sum C_{i j} \log (C_{i j} + e p s)$
Homogeneity	$\sum \frac{C_{i j}}{1 + \| i - j \|}$
Information measures of correlation*	${(1 - \exp [- 2 (h_{i j 2} - h_{i j})])}^{1 / 2}$	h_ij = entropy
		$h_{i j 2} = - \sum p_{i} p_{j} \log (p_{i} p_{j} + e p s)$
		$p_{i} = \sum_{i} C_{i j}$ and $p_{j} = \sum_{j} C_{i j}$
Inverse difference moment	$\sum \frac{C_{i j}}{1 + {(i - j)}^{2}}$

	Fibrous	Calcified	Lipid	Outliers
µ_t	2.3 ± 1.3 mm⁻¹	2.0 ± 0.9 mm⁻¹	8.8 ± 1.6 mm⁻¹	~
Mean	356 ± 135	98 ± 42	253 ± 201	16 ± 7
Contrast	122 ± 93	11 ± 10	89 ± 118	0.6 ± 0.7
Cluster shade	923 ± 2000	59 ± 273*	1667 ± 3312	0.5 ± 5*
Energy	0.012 ± 0.001	0.045 ± 0.031	0.026 ± 0.020	0.328 ± 0.172
Entropy	4.45 ± 0.09	3.52 ± 0.56	4.02 ± 0.57	1.40 ± 0.44
Homogeneity	0.21 ± 0.05	0.44 ± 0.10	0.32 ± 0.13	0.77 ± 0.07
Inverse difference moment	0.97 ± 0.02	0.70 ± 0.16	0.85 ± 0.16	0.18 ± 0.11
Information measures of correlation	0.97 ± 0.01	0.99 ± 0.002	0.98 ± 0.02	1 ± 10⁻⁴

Plaque type	Backscattering	Attenuation
Fibrous	High	Low
Lipid	High+	High
Calcium	Low	Low-

Mean (1st order stat.)	$\frac{1}{N} \sum_{i = 1}^{N} I_{k}$	where N is the number of pixels in a given region and I_k the int. value of a pixel
Co-occurrence probability (2nd order stat.)	$C_{i j} = \frac{P_{i, j}}{\sum_{i, j = 1}^{G} P_{i, j}}$	where P_i,j represents the number of occurrences of gray levels i and j for a certain (d, θ) pair in a given region
Contrast	$\sum C_{i j} {(i - j)}^{2}$	$\sum = \sum_{i = 1}^{G} \sum_{j = 1}^{G}$
Cluster shade	$\sum C_{i j} {(i + j - μ_{i} - μ_{j})}^{3}$	$μ_{i} = \sum i C_{i j}$
Cluster shade	$\sum C_{i j} {(i + j - μ_{i} - μ_{j})}^{3}$	$μ_{j} = \sum j C_{i j}$
Energy	$\sum C_{i j}^{2}$
Entropy*	$- \sum C_{i j} \log (C_{i j} + e p s)$
Homogeneity	$\sum \frac{C_{i j}}{1 + \| i - j \|}$
Information measures of correlation*	${(1 - \exp [- 2 (h_{i j 2} - h_{i j})])}^{1 / 2}$	h_ij = entropy
		$h_{i j 2} = - \sum p_{i} p_{j} \log (p_{i} p_{j} + e p s)$
		$p_{i} = \sum_{i} C_{i j}$ and $p_{j} = \sum_{j} C_{i j}$
Inverse difference moment	$\sum \frac{C_{i j}}{1 + {(i - j)}^{2}}$

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Tables (3)

Equations (6)

Biomedical Optics Express