EchoDiST is an unsupervised framework for estimating myocardial motion in echocardiography, combining self‑distillation joint learning of anatomical segmentation and motion estimation with a diffusion‑conditioned network. The method uses limited anatomical annotations and stochastic perturbations during training, yet requires only a single deterministic forward pass at inference. Evaluated on three datasets, EchoDiST consistently outperforms seven other learning‑based approaches in anatomical alignment, strain assessment, and functional metrics, with statistically significant improvements across cross‑view and cross‑dataset tests.
By Feiyue Qi, Xingyue Wei, Jianwen Luo
arXiv:2610.05603v2 Announce Type: replace
Abstract: Echocardiography is the most widely used cardiac imaging modality, yet interpretation demands integrating visual evidence across global anatomy, lo...
By Sheng Cheng, Donnchadh M. O'Sullivan, Daniel J. Penny, Craig G. Rusin, Minh B. Nguyen, Devika Subramanian
arXiv:2605.05447v2 Announce Type: replace
Abstract: We introduce EchoXFlow, a clinical echocardiography dataset with ultrasound in its native acquisition geometry. Existing public datasets offer limi...
By Elias Stenhede, Joanna Sulkowska, Eivind Bj{\o}rkan Orstad, Henrik Schirmer, Arian Ranjbar
arXiv:2608. 01677v1 Announce Type: cross Abstract: Myocardial strain analysis of cardiac magnetic resonance (CMR) images provides an important tool for evaluating cardiac function.
By Rishov Paul, Frederick H. Epstein, Miaomiao Zhang
The paper introduces ORBIT, a self‑supervised method for detecting end‑diastolic and end‑systolic cardiac phases in fetal echocardiography without manual annotations. ORBIT learns a latent motion trajectory through registration, enabling orientation‑robust identification of phase transitions across diverse fetal heart positions. Evaluated on normal and congenital heart disease cases, it achieves low mean absolute errors (≈1.9–2.4 frames) and outperforms prior annotation‑free approaches that assume fixed orientations.
By Yingyu Yang, Qianye Yang, Can Peng, Elena D'Alberti, Olga Patey, Aris T. Papageorghiou, J. Alison Noble
arXiv:2609.09577v1 Announce Type: cross
Abstract: Myocardial strain from echocardiography is a key biomarker for cardiac function. Recent deep learning methods show strong performance for myocardial...
By Thierry Judge, Nicolas Duchateau, Andreas {\O}stvik, Havard Dalen, Bj{\o}rnar Grenne, Pierre-Yves Courand, Lasse Lovstakken, Pierre-Marc Jodoin, Olivier Bernard