773 citations · 778 across the 9 of their papers we have counts for
9 papers
Myocardial Strain Drift Correction in Deep Learning Based Ultrasound Tracking
Thierry Judge, Nicolas Duchateau, Andreas Østvik +6
Myocardial strain from echocardiography is a key biomarker for cardiac function. Recent deep learning methods show strong performance for myocardial motion tracking but often lack…
Deep Learning Strain Estimation: Is Physics-Based Simulation the Solution?
Thierry Judge, Nicolas Duchateau, Andreas Østvik +13
Speckle tracking echocardiography (STE) is the clinical standard for myocardial strain estimation. Despite good performance on global strain (GLS), its accuracy for regional strain…
EchoTracker2: Enhancing Myocardial Point Tracking by Modeling Local Motion
Md Abulkalam Azad, Vegard Holmstrøm, John Nyberg +4
Myocardial point tracking (MPT) has recently emerged as a promising direction for motion estimation in echocardiography, driven by advances in general-purpose point tracking method…
Taming Modern Point Tracking for Speckle Tracking Echocardiography via Impartial Motion
Md Abulkalam Azad, John Nyberg, Håvard Dalen +3
Accurate motion estimation for tracking deformable tissues in echocardiography is essential for precise cardiac function measurements. While traditional methods like block matching…
Regional quality estimation for echocardiography using deep learning
Gilles Van De Vyver, Svein-Erik Måsøy, Håvard Dalen +7
Automatic estimation of cardiac ultrasound image quality can be beneficial for guiding operators and ensuring the accuracy of clinical measurements. Previous work often fails to di…
EchoTracker: Advancing Myocardial Point Tracking in Echocardiography
Md Abulkalam Azad, Artem Chernyshov, John Nyberg +5
Tissue tracking in echocardiography is challenging due to the complex cardiac motion and the inherent nature of ultrasound acquisitions. Although optical flow methods are considere…