EchoTracker: Advancing Myocardial Point Tracking in Echocardiography
Published in Medical Image Computing and Computer Assisted Intervention (MICCAI 2024) – Marrakesh, Morocco, 2024
EchoTracker is a novel deep learning framework for long-range myocardial point tracking in 2D echocardiographic sequences. By accurately tracking tissue points across cardiac cycles, EchoTracker enables robust motion estimation and provides a foundation for automated myocardial strain analysis — a key clinical indicator of cardiac function.
The paper was early accepted (top 11%) to MICCAI 2024, one of the top conferences in medical image computing.
Authors: M. A. Azad, A. Chernyshov, J. Nyberg, I. Tveten, L. Lovstakken, H. Dalen, B. Grenne, A. Østvik
Venue: MICCAI 2024 – Marrakesh, Morocco (Early Accept, Top 11%)
Code: github.com/riponazad/echotracker
| Access paper | arXiv preprint |
Recommended citation: M. A. Azad, A. Chernyshov, J. Nyberg, I. Tveten, L. Lovstakken, H. Dalen, B. Grenne, and A. Østvik. (2024). "EchoTracker: Advancing Myocardial Point Tracking in Echocardiography." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, Lecture Notes in Computer Science, vol. 15009. Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-031-72083-3_60