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Heart Ventricles

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EchoEFNet: Multi-task deep learning network for automatic calculation of left ventricular ejection fraction in 2D echocardiography.

Computers in biology and medicine
Left ventricular ejection fraction (LVEF) is essential for evaluating left ventricular systolic function. However, its clinical calculation requires the physician to interactively segment the left ventricle and obtain the mitral annulus and apical la...

Robot-Assisted Resection of Left Ventricular Papillary Fibroelastoma Arising From the Mitral Chordal Apparatus.

Innovations (Philadelphia, Pa.)
The application of robot-assisted thoracoscopy to cardiac surgery affords an opportunity to leverage the exceptional intraoperative exposure, visualization, and dexterity of the robotic platform. Here, we report the case of a 72-year-old woman who pr...

An Overview of Deep Learning Methods for Left Ventricle Segmentation.

Computational intelligence and neuroscience
Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images. Deep learning arc...

Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation.

Sensors (Basel, Switzerland)
The segmentation of the left ventricle endocardium (LV) and the left ventricle epicardium (LV) in echocardiography plays an important role in clinical diagnosis. Recently, deep neural networks have been the most commonly used approach for echocardiog...

A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge.

Sensors (Basel, Switzerland)
Automated electrocardiogram (ECG) classification using machine learning (ML) is extensively utilized for arrhythmia detection. Contemporary ML algorithms are typically deployed on the cloud, which may not always meet the availability and privacy requ...

Deep learning can yield clinically useful right ventricular segmentations faster than fully manual analysis.

Scientific reports
Right ventricular (RV) volumes are commonly obtained through time-consuming manual delineations of cardiac magnetic resonance (CMR) images. Deep learning-based methods can generate RV delineations, but few studies have assessed their ability to accel...

Fully automated cardiac MRI segmentation using dilated residual network.

Medical physics
PURPOSE: Cardiac ventricle segmentation from cine magnetic resonance imaging (CMRI) is a recognized modality for the noninvasive assessment of cardiovascular pathologies. Deep learning based algorithms achieved state-of-the-art result performance fro...

Estimation of Cardiac Short Axis Slice Levels with a Cascaded Deep Convolutional and Recurrent Neural Network Model.

Tomography (Ann Arbor, Mich.)
Automatic identification of short axis slice levels in cardiac magnetic resonance imaging (MRI) is important in efficient and precise diagnosis of cardiac disease based on the geometry of the left ventricle. We developed a combined model of convoluti...

Machine Learning-Enabled Fully Automated Assessment of Left Ventricular Volume, Ejection Fraction and Strain: Experience in Pediatric and Young Adult Echocardiography.

Pediatric cardiology
BACKGROUND: Left ventricular (LV) volumes, ejection fraction (EF), and myocardial strain have been shown to be predictive of clinical and subclinical heart disease. Automation of LV functional assessment overcomes difficult technical challenges and c...

IFT-Net: Interactive Fusion Transformer Network for Quantitative Analysis of Pediatric Echocardiography.

Medical image analysis
The task of automatic segmentation and measurement of key anatomical structures in echocardiography is critical for subsequent extraction of clinical parameters. However, the influence of boundary blur, speckle noise, and other factors increase the d...