scholarly journals Automatic Segmentation of Soft Plaque by Modeling the Partial Volume Problem in the Coronary Artery

2010 ◽  
Author(s):  
Mahdi Mazinani ◽  
J. Dehmeshki ◽  
Rahil Hosseini ◽  
T. Ellis ◽  
Salah D. Qanadli
Keyword(s):  
Coronary Artery ◽  
Automatic Segmentation ◽  
Partial Volume ◽  
Volume Problem ◽  
Soft Plaque

scholarly journals Review. Automatic Segmentation Techniques of the Coronary Artery Using CT Images in Acute Coronary Syndromes

2017 ◽  
Vol 3 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Sándor Miklós Szilágyi ◽  
Monica Marton Popovici ◽  
László Szilágyi
Keyword(s):  
Coronary Artery ◽  
Acute Coronary Syndromes ◽  
Vascular System ◽  
Imaging Techniques ◽  
Automatic Segmentation ◽  
Patients At Risk ◽  
Coronary Syndromes ◽  
Artery Disease ◽  
Coronary Artery Tree

AbstractCoronary artery disease represents one of the leading reasons of death worldwide, and acute coronary syndromes are their most devastating consequences. It is extremely important to identify the patients at risk for developing an acute myocardial infarction, and this goal can be achieved using noninvasive imaging techniques. Coronary computed tomography angiography (CCTA) is currently one of the most reliable methods used for assessing the coronary arteries; however, its use in emergency settings is sometimes limited due to time constraints. This paper presents the main characteristics of plaque vulnerability, the role of CCTA in the assessment of vulnerable plaques, and automatic segmentation techniques of the coronary artery tree based on CT angiography images. A detailed inventory of existing methods is given, representing the state-of-the-art of computational methods applied in vascular system segmentation, focusing on the current applications in acute coronary syndromes.

scholarly journals Automatic Segmentation of Coronary Lumen and External Elastic Membrane in IVUS Images Using 8-layer U-Net

2020 ◽  
Author(s):  
Liang Dong ◽  
Wei Lu ◽  
Jun Jiang ◽  
Ya Zhao ◽  
Xiangfen Song ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Tissue Characterization ◽  
Data Augmentation ◽  
Automatic Segmentation ◽  
Model Performance ◽  
Single Frequency ◽  
Elastic Membrane ◽  
Cross Section Area ◽  
Golden Standard ◽  
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.

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

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.

New approach to the automatic segmentation of coronary artery in X-ray angiograms

2008 ◽  
Vol 51 (1) ◽  
pp. 25-39 ◽  
Author(s):  
ShouJun Zhou ◽  
Jun Yang ◽  
WuFan Chen ◽  
YongTian Wang
Keyword(s):  
Coronary Artery ◽  
Automatic Segmentation ◽  
New Approach ◽  
X Ray

Dealing With Noise and Partial Volume Effects in Alzheimer Disease Brain Tissue Classification by a Fuzzy-Possibilistic Modeling Based on Fuzzy-Genetic Initialization

2019 ◽  
Vol 7 (1) ◽  
pp. 119-143 ◽  
Author(s):  
Lilia Lazli ◽  
Mounir Boukadoum
Keyword(s):  
Automatic Segmentation ◽  
Volume Effect ◽  
Performance Comparison ◽  
Partial Volume ◽  
Functional Images ◽  
Possibilistic Clustering ◽  
Quantitative Results ◽  
Fuzzy Techniques ◽  
Brain Data ◽  
Fuzzy Genetic

Segmentation is a key step in brain imaging where clustering techniques are widely used, particularly the fuzzy approach which offers active and robust methods against noise and partial volume effect (PVE). To address those imperfections, this article suggests an automatic segmentation of brain tissues for magnetic resonance and functional images of Alzheimer's patients, based on an efficient and robust genetic-fuzzy-possibilistic clustering scheme for the assessment of white matter, gray matter and cerebrospinal fluid volumes. The proposed hybrid clustering process based on: 1) A fuzzy possibilistic c-means algorithm that models the degree of relationship between each voxel and a given tissue. 2) A fuzzy c-means algorithm to initialize the clusters centers, with subsequent optimization by a genetic algorithm. Each stage of the proposed clustering process is validated on real brain data and synthetic images of an Alzheimer's Disease Neuroimaging Initiative (ADNI) phantom. A performance comparison is made with the usual fuzzy techniques. The visual and quantitative results obtained with the proposed approach using various signal-to-noise ratios prove its effectiveness to quantify the tissue volume of images of different modalities types in the presence of noise and PVE. The effectiveness in terms of computational rate is also demonstrated.

Fully-automatic segmentation of coronary artery using growing algorithm

2020 ◽  
Vol 28 (6) ◽  
pp. 1171-1186
Author(s):  
Jiali Cui ◽  
Hua Guo ◽  
Huafeng Wang ◽  
Fuqiang Chen ◽  
Lixia Shu ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Coronary Arteries ◽  
Automatic Segmentation ◽  
Disease Diagnosis ◽  
Cardiac Computed Tomography Angiography ◽  
Fully Automatic ◽  
Artery Disease ◽  
Growing Strategy ◽  
Coronary Artery Disease Diagnosis

Currently, cardiac computed tomography angiography (CTA) is widely applied to coronary artery disease diagnosis. Automatic segmentation of coronary artery has played an important role in coronary artery disease diagnosis. In this study, we propose and test a fully automatic coronary artery segmentation method that does not require any human-computer interaction. The proposed method uses a growing strategy and contains three main parts namely, (1) the initial seed detection that automatically detects the root points of the left and right coronary arteries where the ascending aorta meets the coronary arteries, (2) the growing strategy that searches for the neighborhood blocks to decide the existence of coronary arteries with an improved convolutional neural network, and (3) the iterative termination condition that decides whether the growing iteration finishes. The proposed framework is validated using a dataset containing 32 cardiac CTA volumes from different patients for training and testing. Experimental results show that the proposed method obtained a Dice loss ranged from 0.70 to 0.83, which indicates that the new method outperforms the traditional methods such as level set.

scholarly journals Automatic segmentation, detection and quantification of coronary artery stenoses on CTA

2013 ◽  
Vol 29 (8) ◽  
pp. 1847-1859 ◽  
Author(s):  
Rahil Shahzad ◽  
Hortense Kirişli ◽  
Coert Metz ◽  
Hui Tang ◽  
Michiel Schaap ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Automatic Segmentation ◽  
Coronary Artery Stenoses ◽  
Detection And Quantification
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