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Few-Shot Learning by a Cascaded Framework With Shape-Constrained Pseudo Label Assessment for Whole Heart Segmentation.

IEEE transactions on medical imaging
Automatic and accurate 3D cardiac image segmentation plays a crucial role in cardiac disease diagnosis and treatment. Even though CNN based techniques have achieved great success in medical image segmentation, the expensive annotation, large memory c...

Dual-Teacher++: Exploiting Intra-Domain and Inter-Domain Knowledge With Reliable Transfer for Cardiac Segmentation.

IEEE transactions on medical imaging
Annotation scarcity is a long-standing problem in medical image analysis area. To efficiently leverage limited annotations, abundant unlabeled data are additionally exploited in semi-supervised learning, while well-established cross-modality data are...

A category attention instance segmentation network for four cardiac chambers segmentation in fetal echocardiography.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Fetal echocardiography is an essential and comprehensive examination technique for the detection of fetal heart anomalies. Accurate cardiac chambers segmentation can assist cardiologists to analyze cardiac morphology and facilitate heart disease diag...

Using synthetic data generation to train a cardiac motion tag tracking neural network.

Medical image analysis
A CNN based method for cardiac MRI tag tracking was developed and validated. A synthetic data simulator was created to generate large amounts of training data using natural images, a Bloch equation simulation, a broad range of tissue properties, and ...

[Artificial Intelligence and teleradiology in cardiovascular imaging by CT-Scan and MRI].

Annales de cardiologie et d'angeiologie
Cardiac CT-Scan and cardiac magnetic resonance imaging (MRI) are two booming cardiac imaging modalities especially in chest pain screening for CT-Scan and in surveillance of patients with known coronary artery disease for MRI. Artificial Intelligence...

Ambulatory Cardiovascular Monitoring Via a Machine-Learning-Assisted Textile Triboelectric Sensor.

Advanced materials (Deerfield Beach, Fla.)
Wearable bioelectronics for continuous and reliable pulse wave monitoring against body motion and perspiration remains a great challenge and highly desired. Here, a low-cost, lightweight, and mechanically durable textile triboelectric sensor that can...

Electromechanical Wave Imaging With Machine Learning for Automated Isochrone Generation.

IEEE transactions on medical imaging
Standard Electromechanical Wave Imaging isochrone generation relies on manual selection of zero-crossing (ZC) locations on incremental strain curves for a number of pixels in the segmented myocardium for each echocardiographic view and patient. When ...

Learning-Based Regularization for Cardiac Strain Analysis via Domain Adaptation.

IEEE transactions on medical imaging
Reliable motion estimation and strain analysis using 3D+ time echocardiography (4DE) for localization and characterization of myocardial injury is valuable for early detection and targeted interventions. However, motion estimation is difficult due to...

Predicting post-operative right ventricular failure using video-based deep learning.

Nature communications
Despite progressive improvements over the decades, the rich temporally resolved data in an echocardiogram remain underutilized. Human assessments reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. A...

X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing.

Biomedical physics & engineering express
This work proposes a pixel-classification approach for vessel segmentation in x-ray angiograms. The proposal uses textural features such as anisotropic diffusion, features based on the Hessian matrix, mathematical morphology and statistics. These fea...