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Heart Ventricles

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Comparative studies of deep learning segmentation models for left ventricle segmentation.

Frontiers in public health
One of the primary factors contributing to death across all age groups is cardiovascular disease. In the analysis of heart function, analyzing the left ventricle (LV) from 2D echocardiographic images is a common medical procedure for heart patients. ...

Domain generalization in deep learning for contrast-enhanced imaging.

Computers in biology and medicine
BACKGROUND: The domain generalization problem has been widely investigated in deep learning for non-contrast imaging over the last years, but it received limited attention for contrast-enhanced imaging. However, there are marked differences in contra...

Fully automated mouse echocardiography analysis using deep convolutional neural networks.

American journal of physiology. Heart and circulatory physiology
Echocardiography (echo) is a translationally relevant ultrasound imaging modality widely used to assess cardiac structure and function in preclinical models of heart failure (HF) during research and drug development. Although echo is a very valuable ...

Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion.

IEEE journal of biomedical and health informatics
Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding labels, to a target image; and the transformed atlas labels can be combi...

Towards fully automated segmentation of rat cardiac MRI by leveraging deep learning frameworks.

Scientific reports
Automated segmentation of human cardiac magnetic resonance datasets has been steadily improving during recent years. Similar applications would be highly useful to improve and speed up the studies of cardiac function in rodents in the preclinical con...

Right ventricular strain and volume analyses through deep learning-based fully automatic segmentation based on radial long-axis reconstruction of short-axis cine magnetic resonance images.

Magma (New York, N.Y.)
OBJECTIVE: We propose a deep learning-based fully automatic right ventricle (RV) segmentation technique that targets radially reconstructed long-axis (RLA) images of the center of the RV region in routine short axis (SA) cardiovascular magnetic reson...

Explicit and automatic ejection fraction assessment on 2D cardiac ultrasound with a deep learning-based approach.

Computers in biology and medicine
BACKGROUND: Ejection fraction (EF) is a key parameter for assessing cardiovascular functions in cardiac ultrasound, but its manual assessment is time-consuming and subject to high inter and intra-observer variability. Deep learning-based methods have...

A Novel Framework With Weighted Decision Map Based on Convolutional Neural Network for Cardiac MR Segmentation.

IEEE journal of biomedical and health informatics
For diagnosing cardiovascular disease, an accurate segmentation method is needed. There are several unresolved issues in the complex field of cardiac magnetic resonance imaging, some of which have been partially addressed by using deep neural network...

Assessment of right ventricular size and function from cardiovascular magnetic resonance images using artificial intelligence.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, despite the complex RV geometry. However, in...