AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Heart

Showing 191 to 200 of 453 articles

Clear Filters

Synthetically trained convolutional neural networks for improved tensor estimation from free-breathing cardiac DTI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cardiac diffusion tensor imaging (cDTI) provides invaluable information about the state of myocardial microstructure. For further clinical dissemination, free-breathing acquisitions are desired, which however require image registration prior to tenso...

A Novel U-Net Based Deep Learning Method for 3D Cardiovascular MRI Segmentation.

Computational intelligence and neuroscience
Medical multiobjective image segmentation aims to group pixels to form multiple regions based on the different properties of the medical images. Segmenting the 3D cardiovascular magnetic resonance (CMR) images is still a challenging task owing to sev...

Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease.

Medical image analysis
Training deep learning models that segment an image in one step typically requires a large collection of manually annotated images that captures the anatomical variability in a cohort. This poses challenges when anatomical variability is extreme but ...

Mutual enhancing learning-based automatic segmentation of CT cardiac substructure.

Physics in medicine and biology
Current segmentation practice for thoracic cancer RT considers the whole heart as a single organ despite increased risks of cardiac toxicities from irradiation of specific cardiac substructures. Segmenting up to 15 different cardiac substructures can...

Physics-constrained deep active learning for spatiotemporal modeling of cardiac electrodynamics.

Computers in biology and medicine
The development of computational modeling and simulation have immensely benefited the study of cardiac disease mechanisms and facilitated the optimal disease diagnosis and treatment design. The dynamic propagation of cardiac electrical signals are of...

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...

Automatic Time-Resolved Cardiovascular Segmentation of 4D Flow MRI Using Deep Learning.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Segmenting the whole heart over the cardiac cycle in 4D flow MRI is a challenging and time-consuming process, as there is considerable motion and limited contrast between blood and tissue.

Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation.

IEEE transactions on medical imaging
Due to the lack of properly annotated medical data, exploring the generalization capability of the deep model is becoming a public concern. Zero-shot learning (ZSL) has emerged in recent years to equip the deep model with the ability to recognize uns...

Automated catheter tip repositioning for intra-cardiac echocardiography.

International journal of computer assisted radiology and surgery
PURPOSE: Intra-Cardiac Echocardiography (ICE) is a powerful imaging modality for guiding cardiac electrophysiology and structural heart interventions. ICE provides real-time observation of anatomy and devices, while enabling direct monitoring of pote...

A multimodal deep learning model for cardiac resynchronisation therapy response prediction.

Medical image analysis
We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the 'nnU-Net' segmentation model ...