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Pericardium

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Automatic Deep Learning Segmentation and Quantification of Epicardial Adipose Tissue in Non-Contrast Cardiac CT scans.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
An Automatic deep learning semantic segmentation (ADLS) using DeepLab-v3-plus technique is proposed for a full and accurate whole heart Epicardial adipose tissue (EAT) segmentation from non-contrast cardiac CT scan. The ADLS algorithm was trained on ...

Automatic left ventricle segmentation in short-axis MRI using deep convolutional neural networks and central-line guided level set approach.

Computers in biology and medicine
In the clinical diagnosis of cardiovascular diseases, left ventricle (LV) segmentation in cardiac magnetic resonance images (MRI) is an indispensable procedure for doctors. To reduce the time needed for diagnosis, we develop an automatic LV segmentat...

Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects.

Circulation. Cardiovascular imaging
BACKGROUND: Epicardial adipose tissue (EAT) volume (cm) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of fully automated deep learning-based EAT volume and attenua...

Adaptive Fruitfly Based Modified Region Growing Algorithm for Cardiac Fat Segmentation Using Optimal Neural Network.

Journal of medical systems
Epicardial adipose tissue is a visceral fat that has remained an entity of concern for decades owing to its high correlation with coronary heart disease. It continues to stump medical practitioners on the pretext of its relevance with pericardial fat...

Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT.

IEEE transactions on medical imaging
Epicardial adipose tissue (EAT) is a visceral fat deposit related to coronary artery disease. Fully automated quantification of EAT volume in clinical routine could be a timesaving and reliable tool for cardiovascular risk assessment. We propose a ne...

A deep learning approach to estimate chemically-treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images.

Acta biomaterialia
UNLABELLED: Biological collagenous tissues comprised of networks of collagen fibers are suitable for a broad spectrum of medical applications owing to their attractive mechanical properties. In this study, we developed a noninvasive approach to estim...

Machine learning in the prediction of cardiac epicardial and mediastinal fat volumes.

Computers in biology and medicine
We propose a methodology to predict the cardiac epicardial and mediastinal fat volumes in computed tomography images using regression algorithms. The obtained results indicate that it is feasible to predict these fats with a high degree of correlatio...