activity
20242026
collaborators

5 papers

eess.IV2026

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…

cs.CV2025

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…

eess.IV2025

Low Complexity Point Tracking of the Myocardium in 2D Echocardiography

Artem Chernyshov, John Nyberg, Vegard Holmstrøm +6

Deep learning methods for point tracking are applicable in 2D echocardiography, but do not yet take advantage of domain specifics that enable extremely fast and efficient configura…

eess.IV2024

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…

cs.CV2024

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…