A bifurcation identifier for IV-OCT using orthogonal least squares and supervised machine learning

Macedo, Maysa M. G.; Guimarães, Welingson V. N.; Galon, Micheli Zanotti; Takimura, Celso Kiyochi; Lemos, Pedro A.; Gutierrez, Marco Antônio

doi:10.1016/j.compmedimag.2015.09.004

Cited by 27 publications

(33 citation statements)

References 28 publications

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“…Briefly, the lumen contour from each IVOCT frame is obtained after two processes based on Macedo et al (2015b): i) preprocessing and ii) lumen contour detection, as shown in Figure 1. The pre-processing stage includes speckle noise removal, catheter and the guide-wire; moreover, a binarization is performed to highlight the intimal layer.…”

Section: Methods To Segment the Vessel Lumenmentioning

confidence: 99%

A robust fully automatic lumen segmentation method for in vivo intracoronary optical coherence tomography

et al. 2016

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Introduction: Intravascular optical coherence tomography (IVOCT) is an in-vivo imaging modality based on the introduction of a catheter in a blood vessel for viewing its inner wall using electromagnetic radiation. One of the most developed automatic applications for this modality is the lumen area segmentation, however on the evaluation of these methods, the slices inside bifurcation regions, or with the presence of complex atherosclerotic plaques and dissections are usually discarded. This paper describes a fully-automatic method for computing the lumen area in IVOCT images where the set of slices includes complex atherosclerotic plaques and dissections. Methods: The proposed lumen segmentation method is divided into two steps: preprocessing, including the removal of artifacts and the second step comprises a lumen detection using morphological operations. In addition, it is proposed an approach to delimit the lumen area for slices inside bifurcation region, considering only the main branch. Results: Evaluation of the automatic lumen segmentation used manual segmentations as a reference, it was performed on 1328 human IVOCT images, presenting a mean difference in lumen area and Dice metrics of 0.19 mm 2 and 97% for slices outside the bifurcation, 1.2 mm 2 and 88% in the regions with bifurcation without automatic contour correction and 0.52 mm 2 and 90% inside bifurcation region with automatic contour correction. Conclusion: This present study shows a robust lumen segmentation method for vessel cross-sections with dissections and complex plaque and bifurcation avoiding the exclusion of such regions from the dataset analysis.

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Section: Methods To Segment the Vessel Lumenmentioning

confidence: 99%

A robust fully automatic lumen segmentation method for in vivo intracoronary optical coherence tomography

et al. 2016

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“…10 In such scenarios, lumen segmentation methods cannot guarantee an accurate lumen contour for the affected regions. Corrupted lumen contours can affect the lumen quantification methods such as geometrical analysis 3,4 of the contour to identify bifurcation points of the side branches. To overcome this, a scheme is presented wherein low-intensity lumen regions of the side branches were identified and subsequently the erroneous contour sections were masked out.…”

Section: Compensation Of Lumen Contour Atmentioning

confidence: 99%

“…1 Automated assessment can reduce this error and produce more consistent image interpretation. Various techniques have been reported for lumen segmentation, [1][2][3][4][5][6][7][8] stent strut detection, 7 and plaque characterization. 1,9 These approaches are either semi or fully automated.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the wall-lumen interface provides a high degree of contrast and this is generally exploited by various techniques to extract the lumen boundary. Some of the methods previously employed include image thresholding, 1 A-line intensity-variation analysis, 2,3 intensity difference-dependent cost function and its minimization approach, 4,5 and labeling methods using Markov random field (MRF). 7 Alternatively, a combination approach with expectation maximization (EM) for labeling and graph-cut for lumen segmentation has been reported.…”

Section: Introductionmentioning

confidence: 99%

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Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method

2016

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, "Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method," J. Abstract. Lumen segmentation from clinical intravascular optical coherence tomography (IV-OCT) images has clinical relevance as it provides a full three-dimensional perspective of diseased coronary artery sections. Inaccurate segmentation may occur when there are artifacts in the image, resulting from issues such as inadequate blood clearance. This study proposes a transmittance-based lumen intensity enhancement method that ensures only lumen regions are highlighted. A level-set-based active contour method that utilizes the local speckle distribution properties of the image is then employed to drive an image-specific active contour toward the true lumen boundaries. By utilizing local speckle properties, the intensity variation issues within the image are resolved. This combined approach has been successfully applied to challenging clinical IV-OCT datasets that contains multiple lumens, residual blood flow, and its shadowing artifact. A method to identify the guide-wire and interpolate the lost lumen segments has been implemented. This approach is fast and can be performed even when guide-wire boundaries are not easily identified. Lumen enhancement also makes it easy to identify vessel side branches. This automated approach is not only able to extract the arterial lumen, but also the smaller microvascular lumens that are associated with the vasa vasorum and with atherosclerotic plaque.

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“…For this study, we used the intensity profile for each column of a 2D IOCT in polar coordinates as input signal for the CWT. Note that this polar image is centered in the lumen centroid, the lumen border was extracted with a method described in [8]. Each column of this image (A-line) is a intensity profile as depicted in Figure 2 b.…”

Section: Multiresolution Analysismentioning

confidence: 99%

Wavelet Analysis for Multiresolution Tissue Characterization in Intracoronary Optical Images

Macedo

Nicz

Campos

et al. 2017

Computing in Cardiology Conference (CinC)

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The rupture of atherosclerotic plaques in coronaries is responsible for most acute coronary events. A new imaging modality called intravascular optical coherence tomography (IOCT) enables a detailed high resolution visualization of micro-structural changes of the in vivo arterial wall and provides an accurate representation of the thickness of a fibrous cap and a useful tool to assess the vulnerability in lipid-rich plaques. This article describes a fully automated method for tissue characterization in IOCT human coronary images.In this study the continous wavelet transform is used to analyze plaque tissue in IOCT frames in polar coordinates, enabling a quantification of fibrous cap thickness. In-vivo human coronary images were used in experimental tests for detecting fibrous cap automatically, where the results were compared with manual segmentations from the same expert in two different instants separated by 2 months.

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A bifurcation identifier for IV-OCT using orthogonal least squares and supervised machine learning

Cited by 27 publications

References 28 publications

A robust fully automatic lumen segmentation method for in vivo intracoronary optical coherence tomography

A robust fully automatic lumen segmentation method for in vivo intracoronary optical coherence tomography

Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method

Wavelet Analysis for Multiresolution Tissue Characterization in Intracoronary Optical Images

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