We propose a novel anomaly detection method for echocardiogram videos. The introduced method takes advantage of the periodic nature of the heart cycle to learn three variants of a variational latent trajectory model (TVAE). While the first two variants (TVAE-C and TVAE-R) model strict periodic movements of the heart, the third (TVAE-S) is more general and allows shifts in the spatial representation throughout the video. All models are trained on the healthy samples of a novel in-house dataset of infant echocardiogram videos consisting of multiple chamber views to learn a normative prior of the healthy population. During inference, maximum a posteriori (MAP) based anomaly detection is performed to detect out-of-distribution samples in our dataset. The proposed method reliably identifies severe congenital heart defects, such as Ebstein's Anomaly or Shone-complex. Moreover, it achieves superior performance over MAP-based anomaly detection with standard variational autoencoders when detecting pulmonary hypertension and right ventricular dilation. Finally, we demonstrate that the proposed method enables interpretable explanations of its output through heatmaps highlighting the regions corresponding to anomalous heart structures.

翻译：我们提出了一种针对超声心动图视频的新型异常检测方法。该方法利用心脏周期的周期性特征，学习变分潜轨迹模型（TVAE）的三种变体。前两种变体（TVAE-C和TVAE-R）对心脏的严格周期性运动进行建模，而第三种变体（TVAE-S）更为通用，允许视频中空间表示发生偏移。所有模型均使用内部收集的、包含多腔切面的婴儿超声心动图视频数据集中的健康样本进行训练，以学习健康人群的规范性先验。在推理阶段，执行基于最大后验概率（MAP）的异常检测，以识别数据集中分布外样本。所提方法能够可靠地识别严重先天性心脏缺陷，如埃布斯坦畸形或Shone综合征。此外，与基于MAP的标准变分自编码器异常检测方法相比，该方法在检测肺动脉高压和右心室扩张方面表现出更优性能。最后，我们证明该方法可通过热力图突出显示异常心脏结构对应区域，为输出结果提供可解释性。