A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment.
A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment.
复制标题
用于无监督心脏形状异常评估的概率深度运动模型。
DOI：
10.1016/j.media.2021.102276
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发表时间：
2022
影响因子：
10.9
通讯作者：
Zakeri A
中科院分区：
文献类型：
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作者：
Zakeri A
大规模成像数据中的自动形状异常检测可用于筛选次优分割和改变心脏形态的病理，而无需密集的体力劳动。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性以预测循环中的下一个形状，从而导出由于网络预测过度偏差而导致的异常点。预测混合分布分别通过高斯分布和均匀分布对内部值和异常值类进行建模。吉布斯采样期望最大化 (EM) 算法通过 E 步骤中每个类别的后验概率计算点的软异常分数，并估计网络参数和 M 步骤中的预测分布。我们使用来自以下两个形状数据集来证明该方法的多功能性：(i) 来自英国生物银行 (UKB) 20,000 名参与者的 100 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) 的常规诊断成像。实验表明，UKB 数据集中检测到的形状异常大多与分割质量较差有关，并且预测的形状序列比输入序列显示出显着的改进。此外，对 M&Ms 数据集中基于 U-Net 的形状的评估表明，异常可归因于影响心室的潜在病理。因此，所提出的模型可以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。
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.