Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound
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Title
Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound
Authors
Keywords
Internal carotid artery, Plaque segmentation, Ultrasound, Hybrid deep learning, Cross-entropy, Dice similarity coefficient, Area, Performance
Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 136, Issue -, Pages 104721
Publisher
Elsevier BV
Online
2021-08-02
DOI
10.1016/j.compbiomed.2021.104721
References
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Note: Only part of the references are listed.- Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease/stroke risk assessment system
- (2021) Luca Saba et al. INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING
- A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort
- (2021) Mohit Agarwal et al. JOURNAL OF MEDICAL SYSTEMS
- Maximum plaque height in carotid ultrasound predicts cardiovascular disease outcomes: a population-based validation study of the American society of echocardiography’s grade II–III plaque characterization and protocol
- (2021) Amer M. Johri et al. INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING
- Wilson disease tissue classification and characterization using seven artificial intelligence models embedded with 3D optimization paradigm on a weak training brain magnetic resonance imaging datasets: a supercomputer application
- (2021) Mohit Agarwal et al. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
- Artificial intelligence in computed tomography plaque characterization: A review
- (2021) Riccardo Cau et al. EUROPEAN JOURNAL OF RADIOLOGY
- Deep Learning-Based Measurement of Total Plaque Area in B-Mode Ultrasound Images
- (2021) Ran Zhou et al. IEEE Journal of Biomedical and Health Informatics
- Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application
- (2021) Luca Saba et al. Annals of Translational Medicine
- Morphological Carotid Plaque Area Is Associated With Glomerular Filtration Rate: A Study of South Asian Indian Patients With Diabetes and Chronic Kidney Disease
- (2020) Anudeep Puvvula et al. ANGIOLOGY
- Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm
- (2020) Gopal S. Tandel et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for cardiovascular/stroke risk assessment
- (2020) Mainak Biswas et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Recommendations for the Assessment of Carotid Arterial Plaque by Ultrasound for the Characterization of Atherosclerosis and Evaluation of Cardiovascular Risk: From the American Society of Echocardiography
- (2020) Amer M. Johri et al. JOURNAL OF THE AMERICAN SOCIETY OF ECHOCARDIOGRAPHY
- 3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular/stroke risk stratification using carotid ultrasound-based delineated plaque: Atheromatic™ 2.0
- (2020) Sanagala S. Skandha et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Integration of estimated glomerular filtration rate biomarker in image-based cardiovascular disease/stroke risk calculator: a south Asian-Indian diabetes cohort with moderate chronic kidney disease
- (2020) Vijay Viswanathan et al. INTERNATIONAL ANGIOLOGY
- Dynamic Carotid Plaque Imaging Using Ultrasound
- (2020) Argyrios A. Giannopoulos et al. JOURNAL OF VASCULAR SURGERY
- CT imaging features of carotid artery plaque vulnerability
- (2020) Alessandro Murgia et al. Annals of Translational Medicine
- Asymptomatic Carotid Stenosis and Risk of Stroke (ACSRS) study: what have we learned from it?
- (2020) Kosmas I. Paraskevas et al. Annals of Translational Medicine
- Deep hybrid neural-like P systems for multiorgan segmentation in head and neck CT/MR images
- (2020) Jie Xue et al. EXPERT SYSTEMS WITH APPLICATIONS
- The present and future of deep learning in radiology
- (2019) Luca Saba et al. EUROPEAN JOURNAL OF RADIOLOGY
- Ultrasound-based carotid stenosis measurement and risk stratification in diabetic cohort: a deep learning paradigm
- (2019) Luca Saba et al. Cardiovascular Diagnosis and Therapy
- Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm
- (2018) Mainak Biswas et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- A Survey on Coronary Atherosclerotic Plaque Tissue Characterization in Intravascular Optical Coherence Tomography
- (2018) Alberto Boi et al. Current Atherosclerosis Reports
- Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers
- (2018) Md. Maniruzzaman et al. JOURNAL OF MEDICAL SYSTEMS
- Morphologic TPA (mTPA) and composite risk score for moderate carotid atherosclerotic plaque is strongly associated with HbA1c in diabetes cohort
- (2018) Elisa Cuadrado-Godia et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients
- (2018) Vasileios Kotsis et al. DIABETES RESEARCH AND CLINICAL PRACTICE
- Association Between Statin Use and Cardiovascular Events After Carotid Artery Revascularization
- (2018) Mohamad A. Hussain et al. Journal of the American Heart Association
- Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm
- (2017) Luca Saba et al. JOURNAL OF MEDICAL SYSTEMS
- An Integrated Approach to Computer-Based Automated Tracing and Its Validation for 200 Common Carotid Arterial Wall Ultrasound Images
- (2017) Filippo Molinari et al. JOURNAL OF ULTRASOUND IN MEDICINE
- Speckle reduction in medical ultrasound images using an unbiased non-local means method
- (2016) P.V. Sudeep et al. Biomedical Signal Processing and Control
- Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: A first comparative study of its kind
- (2016) Vimal K. Shrivastava et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Carotid inter-adventitial diameter is more strongly related to plaque score than lumen diameter: An automated tool for stroke analysis
- (2016) Luca Saba et al. JOURNAL OF CLINICAL ULTRASOUND
- Inter-observer Variability Analysis of Automatic Lung Delineation in Normal and Disease Patients
- (2016) Luca Saba et al. JOURNAL OF MEDICAL SYSTEMS
- A Review on Carotid Ultrasound Atherosclerotic Tissue Characterization and Stroke Risk Stratification in Machine Learning Framework
- (2015) Aditya M. Sharma et al. Current Atherosclerosis Reports
- Automatic Lung Segmentation Using Control Feedback System: Morphology and Texture Paradigm
- (2015) Norliza M. Noor et al. JOURNAL OF MEDICAL SYSTEMS
- Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images
- (2013) U. Rajendra Acharya et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Demographics of carotid atherosclerotic plaque features imaged by computed tomography
- (2013) Jeffrey D. Chien et al. JOURNAL OF NEURORADIOLOGY
- The size of juxtaluminal hypoechoic area in ultrasound images of asymptomatic carotid plaques predicts the occurrence of stroke
- (2013) Stavros K. Kakkos et al. JOURNAL OF VASCULAR SURGERY
- Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment
- (2013) U. Rajendra Acharya et al. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
- Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization
- (2012) U. Rajendra Acharya et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Data mining framework for fatty liver disease classification in ultrasound: A hybrid feature extraction paradigm
- (2012) U. Rajendra Acharya et al. MEDICAL PHYSICS
- Comparison between manual and automated analysis for the quantification of carotid wall by using sonography. A validation study with CT
- (2011) Luca Saba et al. EUROPEAN JOURNAL OF RADIOLOGY
- An Accurate and Generalized Approach to Plaque Characterization in 346 Carotid Ultrasound Scans
- (2011) U. Rajendra Acharya et al. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
- Carotid Plaque Area and Intima-Media Thickness in Prediction of First-Ever Ischemic Stroke
- (2011) Ellisiv B. Mathiesen et al. STROKE
- Intima-media thickness: setting a standard for a completely automated method of ultrasound measurement
- (2010) Filippo Molinari et al. IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL
- AUTOMATIC COMPUTER-BASED TRACINGS (ACT) IN LONGITUDINAL 2-D ULTRASOUND IMAGES USING DIFFERENT SCANNERS
- (2010) FILIPPO MOLINARI et al. Journal of Mechanics in Medicine and Biology
- Asymptomatic internal carotid artery stenosis and cerebrovascular risk stratification
- (2010) Andrew N. Nicolaides et al. JOURNAL OF VASCULAR SURGERY
- Characterization of Single Thyroid Nodules by Contrast-Enhanced 3-D Ultrasound
- (2010) Filippo Molinari et al. ULTRASOUND IN MEDICINE AND BIOLOGY
- Use of Carotid Ultrasound to Identify Subclinical Vascular Disease and Evaluate Cardiovascular Disease Risk: A Consensus Statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force Endorsed by the Society for Vascular Medicine
- (2008) James H. Stein et al. JOURNAL OF THE AMERICAN SOCIETY OF ECHOCARDIOGRAPHY
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