AI 中文总结
研究超声心动图跨数据集左心室分割的域转移问题，利用手工超声描述符等特征估计转移退化，通过几何感知预处理等方法降低其对分割的影响，并能进行特定表示的转移风险估计。
AI 中文摘要
跨数据集泛化仍然是超声心动图左心室分割临床应用的主要障碍，但其转移来源很少被厘清。我们研究了能否在部署前使用手工超声描述符、VAE潜在特征和跨六个超声心动图数据集的分割衍生潜在特征来估计转移退化。几何感知预处理显著改善了几个较差的转移案例，强度z归一化对数据集可分离性影响极小。预测了留出的源 - 目标对的绝对骰子下降情况。不同特征的最具信息性的差异度量不同。供应商效应在很大程度上与数据集混淆。超声心动图域转移是可结构化和测量的，可通过几何感知预处理部分降低其对分割的影响，并通过特定表示的转移风险估计进行预测。
英文摘要
Cross-dataset generalisation remains a major barrier to clinical deployment of echocardiographic left ventricular segmentation, yet the sources of this shift are rarely disentangled. We examined whether transfer degradation could be estimated before deployment using handcrafted ultrasound descriptors, VAE latent features, and segmentation-derived latent features across six echocardiographic datasets. Geometry-aware preprocessing substantially improved several poor transfer cases, suggesting that much of the apparent domain shift reflects field-of-view and framing inconsistencies rather than intrinsic acoustic differences alone. Intensity z-normalisation changed dataset separability by less than 0.005, indicating that brightness and contrast are not the dominant shift axis. Absolute Dice drop on held-out source-target pairs was predicted with an R-squared value of 0.612, an MAE of 0.082, and a Spearman rho of 0.681. The variant without LV and fan-shaped features retained approximately 70% of this explanatory power, supporting mask-free transfer-risk monitoring. The most informative discrepancy measure depended on the representation, with CMD strongest in z-normalised handcrafted features, with an absolute r of approximately 0.86 and an R-squared value of approximately 0.70; log-Wasserstein strongest in VAE space, with an r of approximately -0.90 and an R-squared value of approximately 0.81; and log-MMD strongest in LV-segmentation latent features, with an r of approximately -0.92 and an R-squared value of approximately 0.84. Apparent vendor effects were largely dataset-confounded. Echocardiographic domain shift is therefore structured and measurable, and its impact on segmentation can be partly reduced through geometry-aware preprocessing and anticipated using representation-specific transfer-risk estimation.
