Automatic shape anomaly detection in large-scale imaging data can be useful for screening suboptimal segmentations and pathologies altering the cardiac morphology without intensive manual labour. We propose a deep probabilistic model for local anomaly detection in sequences of heart shapes, modelled as point sets, in a cardiac cycle. A deep recurrent encoder-decoder network captures the spatio-temporal dependencies to predict the next shape in the cycle and thus derive the outlier points that are attributed to excessive deviations from the network prediction. A predictive mixture distribution models the inlier and outlier classes via Gaussian and uniform distributions, respectively. A Gibbs sampling Expectation-Maximisation (EM) algorithm computes soft anomaly scores of the points via the posterior probabilities of each class in the E-step and estimates the parameters of the network and the predictive distribution in the M-step. We demonstrate the versatility of the method using two shape datasets derived from: (i) one million biventricular CMR images from 20,000 participants in the UK Biobank (UKB), and (ii) routine diagnostic imaging from Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac Image (M&Ms). Experiments show that the detected shape anomalies in the UKB dataset are mostly associated with poor segmentation quality, and the predicted shape sequences show significant improvement over the input sequences. Furthermore, evaluations on U-Net based shapes from the M&Ms dataset reveals that the anomalies are attributable to the underlying pathologies that affect the ventricles. The proposed model can therefore be used as an effective mechanism to sift shape anomalies in large-scale cardiac imaging pipelines for further analysis.

大规模成像数据中的自动形状异常检测可用于筛选次优的分割和改变心脏形态的病理，而无需大量的体力劳动。我们提出了一种深度概率模型，用于在心动周期中建模为点集的心脏形状序列中的局部异常检测。深度循环编码器-解码器网络捕获时空依赖关系以预测循环中的下一个形状，从而得出归因于与网络预测的过度偏差的异常点。预测混合分布分别通过高斯分布和均匀分布对内点和异常类进行建模。Gibbs 采样期望最大化 (EM) 算法通过 E 步中每个类别的后验概率计算点的软异常分数，并在 M 步中估计网络参数和预测分布。我们使用两个形状数据集证明了该方法的多功能性，这些数据集来自：(i) 来自英国生物银行 (UKB) 的 20,000 名参与者的一百万张双心室 CMR 图像，以及 (ii) 来自多中心、多供应商和多疾病心脏图像 (M&Ms)。实验表明，UKB数据集中检测到的形状异常大多与分割质量差有关，预测的形状序列比输入序列有显着改善。此外，来自 M& 的基于 U-Net 的形状的评估 Ms 数据集显示异常可归因于影响心室的潜在病理。因此，所提出的模型可用作筛选大规模心脏成像管道中的形状异常以进行进一步分析的有效机制。