AIMC Topic: Heart Ventricles
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Deep Atlas Network for Efficient 3D Left Ventricle Segmentation on Echocardiography.
Medical image analysis
Jan 13, 2020
We proposed a novel efficient method for 3D left ventricle (LV) segmentation on echocardiography, which is important for cardiac disease diagnosis. The proposed method effectively overcame the 3D echocardiography's challenges: high dimensional data, ...
DeepCQ: Deep multi-task conditional quantification network for estimation of left ventricle parameters.
Computer methods and programs in biomedicine
Dec 20, 2019
BACKGROUND AND OBJECTIVE: Automatic cardiac left ventricle (LV) quantification plays an important role in assessing cardiac function. Although many advanced methods have been put forward to quantify related LV parameters, automatic cardiac LV quantif...
Machine learning based quantification of ejection and filling parameters by fully automated dynamic measurement of left ventricular volumes from cardiac magnetic resonance images.
Magnetic resonance imaging
Dec 12, 2019
BACKGROUND: Although analysis of cardiac magnetic resonance (CMR) images provides accurate and reproducible measurements of left ventricular (LV) volumes, these measurements are usually not performed throughout the cardiac cycle because of lack of to...
MFP-Unet: A novel deep learning based approach for left ventricle segmentation in echocardiography.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Oct 28, 2019
Segmentation of the Left ventricle (LV) is a crucial step for quantitative measurements such as area, volume, and ejection fraction. However, the automatic LV segmentation in 2D echocardiographic images is a challenging task due to ill-defined border...
Machine Learning Methods for Automated Quantification of Ventricular Dimensions.
Zebrafish
Oct 3, 2019
Medaka () and zebrafish () contribute substantially to our understanding of the genetic and molecular etiology of human cardiovascular diseases. In this context, the quantification of important cardiac functional parameters is fundamental. We have de...
Improving the clinical understanding of hypertrophic cardiomyopathy by combining patient data, machine learning and computer simulations: A case study.
Morphologie : bulletin de l'Association des anatomistes
Sep 27, 2019
Most patients with hypertrophic cardiomyopathy (HCM), the most common genetic cardiac disease, remain asymptomatic, but others may suffer from sudden cardiac death. A better identification of those patients at risk, together with a better understandi...
PV-LVNet: Direct left ventricle multitype indices estimation from 2D echocardiograms of paired apical views with deep neural networks.
Medical image analysis
Sep 10, 2019
Accurate direct estimation of the left ventricle (LV) multitype indices from two-dimensional (2D) echocardiograms of paired apical views, i.e., paired apical four-chamber (A4C) and two-chamber (A2C), is of great significance to clinically evaluate ca...
A data augmentation approach to train fully convolutional networks for left ventricle segmentation.
Magnetic resonance imaging
Aug 30, 2019
Left ventricle (LV) segmentation plays an important role in the diagnosis of cardiovascular diseases. The cardiac contractile function can be quantified by measuring the segmentation results of LVs. Fully convolutional networks (FCNs) have been prove...
Dynamic pixel-wise weighting-based fully convolutional neural networks for left ventricle segmentation in short-axis MRI.
Magnetic resonance imaging
Aug 26, 2019
Left ventricle (LV) segmentation in cardiac MRI is an essential procedure for quantitative diagnosis of various cardiovascular diseases. In this paper, we present a novel fully automatic left ventricle segmentation approach based on convolutional neu...
Cine MRI analysis by deep learning of optical flow: Adding the temporal dimension.
Computers in biology and medicine
Jul 12, 2019
Accurate segmentation of the left ventricle (LV) from cine magnetic resonance imaging (MRI) is an important step in the reliable assessment of cardiac function in cardiovascular disease patients. Several deep learning convolutional neural network (CN...
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