标题： 成人和胎儿超声心动图视频中无标注的潜在运动轮廓分析 显示英文标题

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

摘要： 心脏阶段的识别是分析和诊断心脏功能的重要步骤。 自动方法，尤其是基于数据的方法进行心脏阶段检测，通常需要大量的注释，这既耗时又费力。 在本文中，我们提出了一种无监督框架，通过从四腔视图超声心动图视频中学习潜在的心脏运动轨迹来进行心室舒张末期（ED）和心室收缩末期（ES）检测。 我们的方法通过训练一个重建模型来编码可解释的时空运动模式，从而消除了对人工注释的需求，包括ED和ES指标、分割或容积测量。 在EchoNet-Dynamic基准上评估，该方法在ED检测中的平均绝对误差（MAE）为3帧（58.3毫秒），在ES检测中的MAE为2帧（38.8毫秒），与最先进的监督方法相当。 扩展到胎儿超声心动图，尽管胎儿心脏模型是由于胎儿心脏定位的变异性而使用非标准化的心脏视图构建的，但该模型在ED检测中的MAE为1.46帧（20.7毫秒），在ES检测中的MAE为1.74帧（25.3毫秒），表现出稳健的性能。 我们的结果展示了所提出的潜在运动轨迹策略在成人和胎儿超声心动图中进行心脏阶段检测的潜力。 这项工作推进了无监督的心脏运动分析，为缺乏注释数据的临床人群提供了一个可扩展的解决方案。 代码将在https://github.com/YingyuYyy/CardiacPhase发布。 显示英文摘要

摘要： 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.