发表机构
Imperial College London; Friedrich–Alexander University Erlangen–Nürnberg; Ultromics Ltd（伦敦帝国理工学院; 弗里德里希-亚历山大大学埃尔朗根-纽伦堡; Ultromics有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于单参数潜在轨道的自监督心脏相位检测方法，通过正弦非线性映射有界相位变量，直接从学习到的相位信号中识别舒张末期和收缩末期，在EchoNet-Dynamic上以更少训练周期达到或超越先前最先进性能。
AI 中文摘要
在超声心动图中准确识别舒张末期（ED）和收缩末期（ES）是量化心室功能的基础，然而手动选择这些关键帧具有主观性，并引入临床上显著的观察者间变异性。近期的自监督方法要么规定严格的周期性轨迹，要么从重建或配准目标中学习无约束的低维运动子空间。前者提供可解释性，但对时间进展施加了限制性假设，而后者则使心脏相位保持隐含，且必须通过后处理几何处理学习到的轨迹来恢复ED/ES。我们将心脏相位是一维信号的生理观察转化为先验，通过将潜在运动分量约束为单参数潜在轨道，即由有界标量相位变量索引的潜在空间中的全局线性轨迹。通过正弦非线性映射该变量，产生具有一致时间顺序的振荡运动信号，从而能够直接从学习到的相位信号中识别ED和ES。这种归纳偏置使模型能够捕捉心脏周期的可解释表示，同时保持捕捉不规则心跳的灵活性。在无标注的EchoNet-Dynamic上训练，我们极简的单参数心脏相位模型学习了有效的潜在轨道，在ED定位方面显著优于先前最先进方法，在ES定位方面与之持平，同时使用更受约束的表示和更少的训练周期。这表明，有原则的生理归纳偏置可以匹配或超越更复杂表示的性能。代码可在以下网址获取：此HTTPS URL。
英文摘要
Accurate identification of end-diastole (ED) and end-systole (ES) in echocardiography underpins the quantification of ventricular function, yet manual selection of these key frames is subjective and introduces clinically significant inter-operator variability. Recent self-supervised methods either prescribe strict periodic trajectories or learn an unconstrained low-dimensional motion subspace from reconstruction or registration objectives. The former offers interpretability but imposes restrictive assumptions on temporal progression, whereas the latter leaves cardiac phase implicit and ED/ES must be recovered through post-hoc geometric processing of the learned trajectory. We translate the physiological observation that cardiac phase is a one-dimensional signal into a prior by constraining the latent motion component to a single-parameter latent orbit, i.e., a global linear trajectory in latent space indexed by a bounded scalar phase variable. Mapping this variable through a sinusoidal nonlinearity yields an oscillatory motion signal with consistent temporal ordering, enabling direct identification of ED and ES from the learned phase signal. This inductive bias allows the model to capture an interpretable representation of the cardiac cycle, while maintaining flexibility to capture irregular heartbeats. Trained on EchoNet-Dynamic without annotations, our minimal single-parameter cardiac phase model learns an effective latent orbit, significantly improves upon the previous state of the art in ED localisation and matches it in ES localisation while using a more constrained representation and fewer training epochs. This demonstrates that a principled physiological inductive bias can match or exceed the performance of more complex representations. Code is available at: https://github.com/BonniciJ/OrbitalEcho/
CommentsAccepted for oral presentation at the ASMUS workshop at MICCAI 2026
