Analysis of Cardiovascular Tissue Components for the Diagnosis of Coronary Vulnerable Plaque from Intravascular Ultrasound Images

The purpose of this study was to characterize cardiovascular tissue components and analyze the different tissue properties for predicting coronary vulnerable plaque from intravascular ultrasound (IVUS) images. For this purpose, sequential IVUS image frames were obtained from human coronary arteries using 20 MHz catheters. The plaque regions between the intima and media-adventitial borders were manually segmented in all IVUS images. Tissue components of the plaque regions were classified into having fibrous tissue (FT), fibrofatty tissue (FFT), necrotic core (NC), or dense calcium (DC). The media area and lumen diameter were also estimated simultaneously. In addition, the external elastic membrane (EEM) was computed to predict the vulnerable plaque after the tissue characterization. The reliability of manual segmentation was validated in terms of inter- and intraobserver agreements. The quantitative results found that the FT and the media as well as the NC would be good indicators for predicting vulnerable plaques in IVUS images. In addition, the lumen was not suitable for early diagnosis of vulnerable plaque because of the low significance compared to the other vessel parameters. To predict vulnerable plaque rupture, future study should have additional experiments using various tissue components, such as the EEM, FT, NC, and media.

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Automatic Segmentation of Coronary Lumen and External Elastic Membrane in Intravascular Ultrasound Images Using 8-layer U-Net

10.21203/rs.3.rs-42239/v3 ◽

2020 ◽

Author(s):

Liang Dong ◽

Wei Lu ◽

Jun Jiang ◽

Ya Zhao ◽

Xiangfen Song ◽

...

Keyword(s):

Coronary Artery ◽

Intravascular Ultrasound ◽

Tissue Characterization ◽

Data Augmentation ◽

Automatic Segmentation ◽

Model Performance ◽

Single Frequency ◽

Elastic Membrane ◽

Ultrasound Images ◽

External Elastic Membrane

Abstract Background: Intravascular ultrasound (IVUS) is the golden standard in accessing the coronary lesions, stenosis, and atherosclerosis plaques. In this paper, a fully-automatic approach by an 8-layer U-Net is developed to segment the coronary artery lumen and the area bounded by external elastic membrane (EEM), i.e. cross section area (EEM-CSA). The database comprises of single-vendor and single-frequency IVUS data. Particularly, the proposed data augmentation of MeshGrid combined with flip and rotation operations is implemented, improving the model performance without pre- or post-processing of the raw IVUS images.Results: The mean intersection of union (mIoU) of 0.937 and 0.804 for the lumen and EEM-CSA respectively were achieved, which exceeded the manual labeling accuracy of the clinician.Conclusion: The accuracy shown by the proposed method is sufficient for subsequent reconstruction of 3D IVUS images, which is essential for doctors’ diagnosis in the tissue characterization of coronary artery walls and plaque compositions, qualitatively and quantitatively.

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Automatic Segmentation of Coronary Lumen and External Elastic Membrane in Intravascular Ultrasound Images Using 8-layer U-Net

10.21203/rs.3.rs-42239/v5 ◽

2021 ◽

Author(s):

Liang Dong ◽

Wenbing Jiang ◽

Wei Lu ◽

Jun Jiang ◽

Ya Zhao ◽

...

Keyword(s):

Coronary Artery ◽

Intravascular Ultrasound ◽

Tissue Characterization ◽

Data Augmentation ◽

Automatic Segmentation ◽

Model Performance ◽

Single Frequency ◽

Elastic Membrane ◽

Ultrasound Images ◽

External Elastic Membrane

Abstract Background: Intravascular ultrasound (IVUS) is the golden standard in accessing the coronary lesions, stenosis, and atherosclerosis plaques. In this paper, a fully-automatic approach by an 8-layer U-Net is developed to segment the coronary artery lumen and the area bounded by external elastic membrane (EEM), i.e. cross section area (EEM-CSA). The database comprises of single-vendor and single-frequency IVUS data. Particularly, the proposed data augmentation of MeshGrid combined with flip and rotation operations is implemented, improving the model performance without pre- or post-processing of the raw IVUS images.Results: The mean intersection of union (MIoU) of 0.937 and 0.804 for the lumen and EEM-CSA respectively were achieved, which exceeded the manual labeling accuracy of the clinician. Conclusion: The accuracy shown by the proposed method is sufficient for subsequent reconstruction of 3D IVUS images, which is essential for doctors’ diagnosis in the tissue characterization of coronary artery walls and plaque compositions, qualitatively and quantitatively.

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Automatic Segmentation of Coronary Lumen and External Elastic Membrane in Intravascular Ultrasound Images Using 8-layer U-Net

10.21203/rs.3.rs-42239/v2 ◽

2020 ◽

Author(s):

Liang Dong ◽

Wei Lu ◽

Jun Jiang ◽

Ya Zhao ◽

Xiangfen Song ◽

...

Keyword(s):

Coronary Artery ◽

Intravascular Ultrasound ◽

Tissue Characterization ◽

Data Augmentation ◽

Automatic Segmentation ◽

Model Performance ◽

Single Frequency ◽

Elastic Membrane ◽

Ultrasound Images ◽

External Elastic Membrane

Abstract Background: Intravascular ultrasound (IVUS) is the golden standard in accessing the coronary lesions, stenosis, and atherosclerosis plaques. In this paper, a fully-automatic approach by an 8-layer U-Net is developed to segment the coronary artery lumen and the area bounded by external elastic membrane, i.e. EEM cross section area (EEM-CSA). The database comprises of single-vendor and single-frequency IVUS data. Particularly, the proposed data augmentation of MeshGrid combined with flip and rotation operations is implemented, improving the model performance without pre- or post-processing of the raw IVUS images.Results: The mean intersection of union (mIoU) of 0.941 and 0.750 for the lumen and EEM-CSA respectively were achieved, which exceeded the manual labeling accuracy of the clinician. Conclusion: The accuracy shown by the proposed method is sufficient for subsequent reconstruction of 3D IVUS images, which is essential for doctors’ diagnosis in the tissue characterization of coronary artery walls and plaque compositions, qualitatively and quantitatively.

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Automatic Segmentation of Coronary Lumen and External Elastic Membrane in Intravascular Ultrasound Images Using 8-layer U-Net

10.21203/rs.3.rs-42239/v4 ◽

2021 ◽

Author(s):

Liang Dong ◽

Wei Lu ◽

Jun Jiang ◽

Ya Zhao ◽

Xiangfen Song ◽

...

Keyword(s):

Coronary Artery ◽

Intravascular Ultrasound ◽

Tissue Characterization ◽

Data Augmentation ◽

Automatic Segmentation ◽

Model Performance ◽

Single Frequency ◽

Elastic Membrane ◽

Ultrasound Images ◽

External Elastic Membrane

Abstract Background: Intravascular ultrasound (IVUS) is the golden standard in accessing the coronary lesions, stenosis, and atherosclerosis plaques. In this paper, a fully-automatic approach by an 8-layer U-Net is developed to segment the coronary artery lumen and the area bounded by external elastic membrane (EEM), i.e. cross section area (EEM-CSA). The database comprises of single-vendor and single-frequency IVUS data. Particularly, the proposed data augmentation of MeshGrid combined with flip and rotation operations is implemented, improving the model performance without pre- or post-processing of the raw IVUS images.Results: The mean intersection of union (mIoU) of 0.937 and 0.804 for the lumen and EEM-CSA respectively were achieved, which exceeded the manual labeling accuracy of the clinician. Conclusion: The accuracy shown by the proposed method is sufficient for subsequent reconstruction of 3D IVUS images, which is essential for doctors’ diagnosis in the tissue characterization of coronary artery walls and plaque compositions, qualitatively and quantitatively.

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Automatic segmentation of coronary lumen and external elastic membrane in intravascular ultrasound images using 8-layer U-Net

BioMedical Engineering OnLine ◽

10.1186/s12938-021-00852-0 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Liang Dong ◽

Wenbing Jiang ◽

Wei Lu ◽

Jun Jiang ◽

Ya Zhao ◽

...

Keyword(s):

Coronary Artery ◽

Intravascular Ultrasound ◽

Tissue Characterization ◽

Data Augmentation ◽

Model Performance ◽

Single Frequency ◽

Elastic Membrane ◽

Ultrasound Images ◽

Cross Sectional ◽

External Elastic Membrane

Abstract Background Intravascular ultrasound (IVUS) is the golden standard in accessing the coronary lesions, stenosis, and atherosclerosis plaques. In this paper, a fully automatic approach by an 8-layer U-Net is developed to segment the coronary artery lumen and the area bounded by external elastic membrane (EEM), i.e., cross-sectional area (EEM-CSA). The database comprises single-vendor and single-frequency IVUS data. Particularly, the proposed data augmentation of MeshGrid combined with flip and rotation operations is implemented, improving the model performance without pre- or post-processing of the raw IVUS images. Results The mean intersection of union (MIoU) of 0.937 and 0.804 for the lumen and EEM-CSA, respectively, were achieved, which exceeded the manual labeling accuracy of the clinician. Conclusion The accuracy shown by the proposed method is sufficient for subsequent reconstruction of 3D-IVUS images, which is essential for doctors’ diagnosis in the tissue characterization of coronary artery walls and plaque compositions, qualitatively and quantitatively.

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Detecting the Media-adventitia Border in Intravascular Ultrasound Images through a Classification-based Approach

Ultrasonic Imaging ◽

10.1177/0161734618820112 ◽

2018 ◽

Vol 41 (2) ◽

pp. 78-93 ◽

Cited By ~ 1

Author(s):

Yuan-yuan Wang ◽

Chen-hui Qiu ◽

Jun Jiang ◽

Shun-ren Xia

Keyword(s):

Intravascular Ultrasound ◽

Feature Detection ◽

Guide Wire ◽

Disease Diagnosis ◽

Rectangular Domain ◽

Ultrasound Images ◽

Snake Model ◽

External Force Field ◽

The Media ◽

Learning Machine

The detection of the media-adventitia (MA) border in intravascular ultrasound (IVUS) images is essential for vessel assessment and disease diagnosis. However, it remains a challenging task, considering the existence of plaque, calcification, and various artifacts. In this article, an effective method based on classification is proposed to extract the MA border in IVUS images. First, a novel morphologic feature describing the relative position of each structure relative to the MA border, called RPES for short, is proposed. Then, the RPES feature and other features are employed in a multiclass extreme learning machine (ELM) to classify IVUS images into nine classes including the MA border and other structures. At last, a modified snake model is employed to effectively detect the MA border in the rectangular domain, in which a modified external force field is constructed on the basis of local border appearances and classification results. The proposed method is evaluated on a public dataset with 77 IVUS images by three indicators in eight situations, such as calcification and a guide wire artifact. With the proposed RPES feature, detection performances are improved by more than 39 percent, which shows an apparent advantage in comparative experiments. Furthermore, compared with two other existing methods used on the same dataset, the proposed method achieves 18 of the best indicators among 24, demonstrating its higher capability in detecting the MA border.

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Diagnostic Accuracy of Optical Coherence Tomography and Integrated Backscatter Intravascular Ultrasound Images for Tissue Characterization of Human Coronary Plaques

Journal of the American College of Cardiology ◽

10.1016/j.jacc.2006.02.062 ◽

2006 ◽

Vol 48 (1) ◽

pp. 81-88 ◽

Cited By ~ 170

Author(s):

Masanori Kawasaki ◽

Brett E. Bouma ◽

Jason Bressner ◽

Stuart L. Houser ◽

Seemantini K. Nadkarni ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Diagnostic Accuracy ◽

Intravascular Ultrasound ◽

Tissue Characterization ◽

Ultrasound Images ◽

Optical Coherence ◽

Integrated Backscatter ◽

Integrated Backscatter Intravascular Ultrasound

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Relation Between Wall Shear Stress and Plaque Necrotic Core in the Left Coronary Arteries of Patients Using Intravascular Ultrasound and Computational Fluid Dynamics

ASME 2008 Summer Bioengineering Conference, Parts A and B ◽

10.1115/sbc2008-192382 ◽

2008 ◽

Author(s):

Jin Suo ◽

Michael McDaniel ◽

Habib Samady ◽

Don Giddens

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Shear Stress ◽

Wall Shear Stress ◽

Intravascular Ultrasound ◽

Coronary Arteries ◽

Plaque Rupture ◽

Necrotic Core ◽

Elastic Membrane ◽

Wall Shear

Atherosclerosis is a disease characterized by arterial plaques that include several components of which the necrotic core has been recognized as an important indicator of the likelihood of plaque rupture [1]. In the present study, the relation of hemodynamic wall shear stress (WSS) to necrotic core localization in the left coronary artery of patients was investigated using intravascular ultrasound (IVUS) and computational fluid dynamics (CFD). An innovative 3D measuring technique was developed and was successfully used to reconstruct coronary arteries in patients based on angiographic images and echo ultrasound slices from IVUS. The reconstruction includes lumen, external elastic membrane (EEM) and spatial distribution of plaque components such as fibrous tissue, necrotic core and calcium. WSS distribution in the vessel segment was computed by CFD, and the relative locations of necrotic core and WSS were determined. Results to date support the hypothesis that a greater necrotic core in coronary plaques is associated with areas of low WSS. The methodology developed has implications for the study of plaque progression and the prediction of likelihood of plaque rupture.

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