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Heart Ventricles

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Constrained-CNN losses for weakly supervised segmentation.

Medical image analysis
Weakly-supervised learning based on, e.g., partially labelled images or image-tags, is currently attracting significant attention in CNN segmentation as it can mitigate the need for full and laborious pixel/voxel annotations. Enforcing high-order (gl...

Automated, machine learning-based, 3D echocardiographic quantification of left ventricular mass.

Echocardiography (Mount Kisco, N.Y.)
BACKGROUND: Although 3D echocardiography (3DE) circumvents many limitations of 2D echocardiography by allowing direct measurements of left ventricular (LV) mass, it is seldom used in clinical practice due to time-consuming analysis. A recently develo...

Semiautomatic Three-Dimensional Threshold-Based Cardiac Computed Tomography Ventricular Volumetry in Repaired Tetralogy of Fallot: Comparison with Cardiac Magnetic Resonance Imaging.

Korean journal of radiology
OBJECTIVE: To assess the accuracy and potential bias of computed tomography (CT) ventricular volumetry using semiautomatic three-dimensional (3D) threshold-based segmentation in repaired tetralogy of Fallot, and to compare them to those of two-dimens...

Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images.

Magma (New York, N.Y.)
OBJECTIVE: The aim of this paper is to investigate the use of fully convolutional neural networks (FCNNs) to segment scar tissue in the left ventricle from cardiac magnetic resonance with late gadolinium enhancement (CMR-LGE) images.

Direct Segmentation-Based Full Quantification for Left Ventricle via Deep Multi-Task Regression Learning Network.

IEEE journal of biomedical and health informatics
Quantitative analysis of the heart is extremely necessary and significant for detecting and diagnosing heart disease, yet there are still some challenges. In this study, we propose a new end-to-end segmentation-based deep multi-task regression learni...

Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy.

European journal of heart failure
AIMS: We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to card...

Deep Learning-based Method for Fully Automatic Quantification of Left Ventricle Function from Cine MR Images: A Multivendor, Multicenter Study.

Radiology
Purpose To develop a deep learning-based method for fully automated quantification of left ventricular (LV) function from short-axis cine MR images and to evaluate its performance in a multivendor and multicenter setting. Materials and Methods This r...

Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely used to mitigate attenuation artifacts. We evaluated the prediction of obstructive disease...