AIMC Topic: Heart Ventricles

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HALSR-Net: Improving CNN Segmentation of Cardiac Left Ventricle MRI with Hybrid Attention and Latent Space Reconstruction.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate cardiac MRI segmentation is vital for detailed cardiac analysis, yet the manual process is labor-intensive and prone to variability. Despite advancements in MRI technology, there remains a significant need for automated methods that can reli...

Deep learning-based automated segmentation of cardiac real-time MRI in non-human primates.

Computers in biology and medicine
Advanced imaging techniques, like magnetic resonance imaging (MRI), have revolutionised cardiovascular disease diagnosis and monitoring in humans and animal models. Real-time (RT) MRI, which can capture a single slice during each consecutive heartbea...

3D velocity and pressure field reconstruction in the cardiac left ventricle via physics informed neural network from echocardiography guided by 3D color Doppler.

Computer methods and programs in biomedicine
Fluid dynamics of the heart chamber can provide critical biological cues for understanding cardiac health and disease and have the potential for supporting diagnosis and prognosis. However, directly acquiring fluid dynamics information from clinical ...

Neural-symbolic hybrid model for myosin complex in cardiac ventriculum decodes structural bases for inheritable heart disease from its genetic encoding.

Archives of biochemistry and biophysics
BACKGROUND: Human ventriculum myosin (βmys) powers contraction sometimes in complex with myosin binding protein C (MYBPC3). The latter regulates βmys activity and impacts cardiac function. Single residue variants (SRVs) change protein sequence in βmy...

Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study.

PloS one
This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of im...

Reliability of post-contrast deep learning-based highly accelerated cardiac cine MRI for the assessment of ventricular function.

Magnetic resonance imaging
OBJECTIVE: The total examination time can be reduced if high-quality two-dimensional (2D) cine images can be collected post-contrast to minimize non-scanning time prior to late gadolinium-enhanced imaging. This study aimed to assess the equivalency o...

Super-resolution left ventricular flow and pressure mapping by Navier-Stokes-informed neural networks.

Computers in biology and medicine
Intraventricular vector flow mapping (VFM) is an increasingly adopted echocardiographic technique that derives time-resolved two-dimensional flow maps in the left ventricle (LV) from color-Doppler sequences. Current VFM models rely on kinematic const...

Deep-DM: Deep-Driven Deformable Model for 3D Image Segmentation Using Limited Data.

IEEE journal of biomedical and health informatics
Objective - Medical image segmentation is essential for several clinical tasks, including diagnosis, surgical and treatment planning, and image-guided interventions. Deep Learning (DL) methods have become the state-of-the-art for several image segmen...

BEAS-Net: A Shape-Prior-Based Deep Convolutional Neural Network for Robust Left Ventricular Segmentation in 2-D Echocardiography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Left ventricle (LV) segmentation of 2-D echocardiography images is an essential step in the analysis of cardiac morphology and function and-more generally-diagnosis of cardiovascular diseases (CVD). Several deep learning (DL) algorithms have recently...