AIMC Topic: Heart Ventricles

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Improving the generalization of deep learning methods to segment the left ventricle in short axis MR images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Cardiovascular disease is one of the major health problems worldwide. In clinical practice, cardiac magnetic resonance imaging (CMR) is considered the gold-standard imaging modality for the evaluation of the function and structure of the left ventric...
Deep Learning Magnetic Resonance Imaging Heart Heart Ventricles Algorithms
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Automated Segmentation of Left Ventricular Myocardium on Cardiac Computed Tomography Using Deep Learning.

Korean journal of radiology Jun 1, 2020
OBJECTIVE: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT.
Male Middle Aged Deep Learning Coronary Artery Disease Female Aged Sensitivity and Specificity Heart Ventricles Automation Humans Algorithms Retrospective Studies Tomography, X-Ray Computed
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Automatic left ventricle segmentation from cardiac magnetic resonance images using a capsule network.

Journal of X-ray science and technology Jan 1, 2020
PURPOSE: Segmentation of magnetic resonance images (MRI) of the left ventricle (LV) plays a key role in quantifying the volumetric functions of the heart, such as the area, volume, and ejection fraction. Traditionally, LV segmentation is performed ma...
Magnetic Resonance Imaging Heart Ventricles Humans Image Interpretation, Computer-Assisted Algorithms Deep Learning
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Right Ventricular Segmentation from MRI Using Deep Convolutional Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
The assessment of right ventricular (RV) function is essential in the diagnosis of many cardiac diseases. Magnetic resonance imaging (MRI) offers an excellent solution to image right ventricle non-invasively with high contrast and temporal resolution...
Humans Neural Networks, Computer Machine Learning Heart Ventricles Algorithms Image Processing, Computer-Assisted Magnetic Resonance Imaging
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A Multi-channel Deep Learning Approach for Segmentation of the Left Ventricular Endocardium from Cardiac Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
Cardiac segmentation is the first most important step in assessing cardiac diseases. However, it still remains challenging owing to the complicated information of myocardium's boundary. In this work, we investigate approaches based on deep learning f...
Humans Neural Networks, Computer Algorithms Endocardium Deep Learning Image Processing, Computer-Assisted Heart Ventricles
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Machine learning based automated dynamic quantification of left heart chamber volumes.

European heart journal. Cardiovascular Imaging May 1, 2019
AIMS: Studies have demonstrated the ability of a new automated algorithm for volumetric analysis of 3D echocardiographic (3DE) datasets to provide accurate and reproducible measurements of left ventricular and left atrial (LV, LA) volumes at end-syst...
Echocardiography, Three-Dimensional Heart Ventricles Heart Atria Image Interpretation, Computer-Assisted Humans Machine Learning Female Reproducibility of Results Male Middle Aged Magnetic Resonance Imaging
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Network Tomography for Understanding Phenotypic Presentations in Aortic Stenosis.

JACC. Cardiovascular imaging Feb 1, 2019
OBJECTIVES: This study sought to build a patient-patient similarity network using multiple features of left ventricular (LV) structure and function in patients with aortic stenosis (AS). The study further validated the observations in an experimental...
Heart Ventricles Image Interpretation, Computer-Assisted Heart Valve Prosthesis Implantation Balloon Valvuloplasty Humans Aortic Valve Echocardiography Machine Learning Predictive Value of Tests Middle Aged Severity of Illness Index Cross-Sectional Studies Mice, Knockout Disease Progression Time Factors Animals Disease Models, Animal Reproducibility of Results Male Aortic Valve Stenosis Aged Ventricular Function, Left Aged, 80 and over Female
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The Mechanics of Machine Learning: From a Concept to Value.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography Dec 1, 2018
Machine Learning Algorithms Heart Ventricles Heart Failure Humans
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Computational Platform Based on Deep Learning for Segmenting Ventricular Endocardium in Long-axis Cardiac MR Imaging.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
This paper presents an automated computational platform based on deep learning (DL) approach for left ventricular (LV) and right ventricular (RV) endocardium segmentation in long-axis cine cardiovascular magnetic resonance (CMR). The proposed method ...
Models, Cardiovascular Magnetic Resonance Imaging, Cine Endocardium Humans Deep Learning Neural Networks, Computer Heart Ventricles Algorithms Image Interpretation, Computer-Assisted Reproducibility of Results
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Machine-Learning Prediction of Drug-Induced Cardiac Arrhythmia: Analysis of Gene Expression and Clustering.

Critical reviews in biomedical engineering Jan 1, 2018
A marked delay in the electrical repolarization of heart ventricles is characterized by prolongation of the Q-T wave (QT) interval on a surface electrocardiogram. Such a delay can lead to potentially life-threatening cardiac arrhythmia (torsades de p...
Heart Ventricles Arrhythmias, Cardiac MAP Kinase Signaling System Gene Expression Regulation Signal Processing, Computer-Assisted Drug Design Heart Gene Expression Profiling Drug Evaluation, Preclinical Mice Rats Clinical Trials as Topic Electrocardiography False Positive Reactions Support Vector Machine Humans Torsades de Pointes Cluster Analysis Machine Learning Female Reproducibility of Results Sensitivity and Specificity Animals Male
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