Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos
Journal:
arXiv
Published Date:
Jul 7, 2025
Abstract
The identification of cardiac phase is an essential step for analysis and
diagnosis of cardiac function. Automatic methods, especially data-driven
methods for cardiac phase detection, typically require extensive annotations,
which is time-consuming and labor-intensive. In this paper, we present an
unsupervised framework for end-diastole (ED) and end-systole (ES) detection
through self-supervised learning of latent cardiac motion trajectories from
4-chamber-view echocardiography videos. Our method eliminates the need for
manual annotations, including ED and ES indices, segmentation, or volumetric
measurements, by training a reconstruction model to encode interpretable
spatiotemporal motion patterns. Evaluated on the EchoNet-Dynamic benchmark, the
approach achieves mean absolute error (MAE) of 3 frames (58.3 ms) for ED and 2
frames (38.8 ms) for ES detection, matching state-of-the-art supervised
methods. Extended to fetal echocardiography, the model demonstrates robust
performance with MAE 1.46 frames (20.7 ms) for ED and 1.74 frames (25.3 ms) for
ES, despite the fact that the fetal heart model is built using non-standardized
heart views due to fetal heart positioning variability. Our results demonstrate
the potential of the proposed latent motion trajectory strategy for cardiac
phase detection in adult and fetal echocardiography. This work advances
unsupervised cardiac motion analysis, offering a scalable solution for clinical
populations lacking annotated data. Code will be released at
https://github.com/YingyuYyy/CardiacPhase.
