AIMC Topic: Heart Ventricles
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Using machine learning to characterize heart failure across the scales.
Biomechanics and modeling in mechanobiology
Jun 25, 2019
Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in time and space. Multiscale models of cardiac growth can provide a patient-specific window into the p...
Machine Learning-Based Three-Dimensional Echocardiographic Quantification of Right Ventricular Size and Function: Validation Against Cardiac Magnetic Resonance.
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
Jun 4, 2019
BACKGROUND: Three-dimensional echocardiography (3DE) allows accurate and reproducible measurements of right ventricular (RV) size and function. However, widespread implementation of 3DE in routine clinical practice is limited because the existing sof...
Quantitative CMR population imaging on 20,000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation.
Medical image analysis
May 25, 2019
Population imaging studies generate data for developing and implementing personalised health strategies to prevent, or more effectively treat disease. Large prospective epidemiological studies acquire imaging for pre-symptomatic populations. These st...
Unsupervised classification of multi-omics data during cardiac remodeling using deep learning.
Methods (San Diego, Calif.)
Mar 7, 2019
Integration of multi-omics in cardiovascular diseases (CVDs) presents high potentials for translational discoveries. By analyzing abundance levels of heterogeneous molecules over time, we may uncover biological interactions and networks that were pre...
Left ventricle segmentation in the era of deep learning.
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Mar 4, 2019
Constrained-CNN losses for weakly supervised segmentation.
Medical image analysis
Feb 13, 2019
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.)
Dec 28, 2018
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
Dec 27, 2018
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.)
Nov 20, 2018
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
Nov 1, 2018
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...
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