AIMC Topic: Heart Ventricles

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Structure Preserving Cycle-Gan for Unsupervised Medical Image Domain Adaptation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The presence of domain shift in medical imaging is a common issue, which can greatly impact the performance of segmentation models when dealing with unseen image domains. This work introduces the Structure Preserving Cycle-GAN (SP Cycle-GAN) for unsu...

CBAM_SAUNet: A novel attention U-Net for effective segmentation of corner cases.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
U-Net has been demonstrated to be effective for the task of medical image segmentation. Additionally, integrating attention mechanism into U-Net has been shown to yield significant benefits. The Shape Attentive U-Net (SAUNet) is one such recently pro...

Deep Left Ventricular Motion Estimation Methods in Echocardiography: A Comparative Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Motion estimation in echocardiography is critical when assessing heart function and calculating myocardial deformation indices. Nevertheless, there are limitations in clinical practice, particularly with regard to the accuracy and reliability of meas...

A DenseNet-based Abnormal Ventricular Potentials Onset Delineation: A Feasibility Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Abnormal ventricular potentials (AVPs) are fractionated and complex electrograms (EGMs), typically associated with slow conduction areas in the myocardium. As such, in ventricular tachycardia (VT), their identification supports the localization of th...

Deep learning to detect left ventricular structural abnormalities in chest X-rays.

European heart journal
BACKGROUND AND AIMS: Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportuni...

Deep learning for automatic volumetric segmentation of left ventricular myocardium and ischaemic scar from multi-slice late gadolinium enhancement cardiovascular magnetic resonance.

European heart journal. Cardiovascular Imaging
AIMS: This study details application of deep learning for automatic volumetric segmentation of left ventricular (LV) myocardium and scar and automated quantification of myocardial ischaemic scar burden from late gadolinium enhancement cardiovascular ...

The Study of Echocardiography of Left Ventricle Segmentation Combining Transformer and Convolutional Neural Networks.

International heart journal
Accurate prediction of echocardiographic parameters is essential for diagnosis and treatment of cardiac disease, especially for segmentation of the left ventricle to obtain measurements such as left ventricular ejection fraction and volume. However, ...

Fine grained automatic left ventricle segmentation via ROI based Tri-Convolutional neural networks.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: The left ventricle segmentation (LVS) is crucial to the assessment of cardiac function. Globally, cardiovascular disease accounts for the majority of deaths, posing a significant health threat. In recent years, LVS has gained important at...