5 papers · 1 filter
The MYOSAIQ Challenge: Myocardial Segmentation with Automated Infarct Quantification
Olivier Bernard, William A. Romero R., Cyprien Bouton +24
Late gadolinium enhancement (LGE) cardiac magnetic resonance (MR) imaging is the modality of choice to assess myocardial infarction (MI) lesions. Nowadays MI volume quantification…
Boosting Cardiac Color Doppler Frame Rates with Deep Learning
Julia Puig, Denis Friboulet, Hang Jung Ling +5
Color Doppler echocardiography enables visualization of blood flow within the heart. However, the limited frame rate impedes the quantitative assessment of blood velocity throughou…
Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping
Hang Jung Ling, Salomé Bru, Julia Puig +6
Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional iVFM opt…
Phase Unwrapping of Color Doppler Echocardiography using Deep Learning
Hang Jung Ling, Olivier Bernard, Nicolas Ducros +1
Color Doppler echocardiography is a widely used non-invasive imaging modality that provides real-time information about the intracardiac blood flow. In an apical long-axis view of…
Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use?
Hang Jung Ling, Nathan Painchaud, Pierre-Yves Courand +3
Deep learning-based methods have spearheaded the automatic analysis of echocardiographic images, taking advantage of the publication of multiple open access datasets annotated by e…