# Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos

arXiv: https://arxiv.org/abs/2507.05154 · HTML: https://mlsanity.com/paper/2507.05154/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [Latent Image Animator: Learning to Animate Images via Latent Space Navigation](https://mlsanity.com/paper/2203.09043/) (2022)
- [Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation](https://mlsanity.com/paper/2107.00977/) (2021)
- [Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models](https://mlsanity.com/paper/2206.15316/) (2022)
- [DDSB: An Unsupervised and Training-free Method for Phase Detection in Echocardiography](https://mlsanity.com/paper/2403.12787/) (2024)

## Built on by (most cited first, 6 of 6)

- [Orientation-Robust Latent Motion Trajectory Learning for Annotation-free Cardiac Phase Detection in Fetal Echocardiography](https://mlsanity.com/paper/2602.06761/) (2026)
- [Low-Rank-Modulated Functa: Exploring the Latent Space of Implicit Neural Representations for Interpretable Ultrasound Video Analysis](https://mlsanity.com/paper/2603.25951/) (2026)
- [FrameONE: Hierarchical Motion Modeling for Universal Multi-View Echocardiographic Keyframe Detection](https://mlsanity.com/paper/2607.00748/) (2026)
- [A Unified DINOv2-Based Framework for LVEF Estimation, GLS Dysfunction Classification, and Early Cardiotoxicity Prediction](https://mlsanity.com/paper/2608.14750/) (2026)
- [Phase-Aligned Finite-Fourier Periodic Deformation for 4D Medical Image Interpolation](https://mlsanity.com/paper/2608.24027/) (2026)
- [Self-Supervised Cardiac Phase Detection via Single-Parameter Latent Orbits](https://mlsanity.com/paper/2609.11650/) (2026)

## Abstract (arXiv)

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.