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Heart Ventricles

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Non-ischemic endocardial scar geometric remodeling toward topological machine learning.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Scar tissues have been important factors in determining the progression of myocardial diseases and the development of adverse cardiac failure outcomes. Accurate segmentation of the scar tissues can be helpful to the clinicians for risk prediction and...

Automatic left ventricle segmentation in short-axis MRI using deep convolutional neural networks and central-line guided level set approach.

Computers in biology and medicine
In the clinical diagnosis of cardiovascular diseases, left ventricle (LV) segmentation in cardiac magnetic resonance images (MRI) is an indispensable procedure for doctors. To reduce the time needed for diagnosis, we develop an automatic LV segmentat...

Localization of origins of premature ventricular contraction in the whole ventricle based on machine learning and automatic beat recognition from 12-lead ECG.

Physiological measurement
OBJECTIVE: The localization of origins of premature ventricular contraction (PVC) is the key factor for the success of ablation of ventricular arrhythmias. Existing methods rely heavily on manual extraction of PVC beats, which limits their applicatio...

Deep Learning for Improved Risk Prediction in Surgical Outcomes.

Scientific reports
The Norwood surgical procedure restores functional systemic circulation in neonatal patients with single ventricle congenital heart defects, but this complex procedure carries a high mortality rate. In this study we address the need to provide an acc...

Automated detection of left ventricle in arterial input function images for inline perfusion mapping using deep learning: A study of 15,000 patients.

Magnetic resonance in medicine
PURPOSE: Quantification of myocardial perfusion has the potential to improve the detection of regional and global flow reduction. Significant effort has been made to automate the workflow, where one essential step is the arterial input function (AIF)...

Deep Learning Single-Frame and Multiframe Super-Resolution for Cardiac MRI.

Radiology
Background Cardiac MRI is limited by long acquisition times, yet faster acquisition of smaller-matrix images reduces spatial detail. Deep learning (DL) might enable both faster acquisition and higher spatial detail via super-resolution. Purpose To ex...

Left ventricle automatic segmentation in cardiac MRI using a combined CNN and U-net approach.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cardiovascular diseases can be effectively prevented from worsening through early diagnosis. To this end, various methods have been proposed to detect the disease source by analyzing cardiac magnetic resonance images (MRI), wherein left ventricular s...

Cardiac MR segmentation based on sequence propagation by deep learning.

PloS one
Accurate segmentation of myocardial in cardiac MRI (magnetic resonance image) is key to effective rapid diagnosis and quantitative pathology analysis. However, a low-quality CMR (cardiac magnetic resonance) image with a large amount of noise makes it...

A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cardiac MRI has been widely used for noninvasive assessment of cardiac anatomy and function as well as heart diagnosis. The estimation of physiological heart parameters for heart diagnosis essentially require accurate segmentation of the Left ventric...