通过轮廓采样量化超声心动图临床指标的不确定性，提升自动化分析可信度。
Uncertainty Propagation for Echocardiography Clinical Metric Estimation via Contour Sampling
- 基于轮廓而非分割预测位置不确定性，生成多组轮廓样本。
- 在两个心脏超声数据集上实现临床指标不确定性准确估计。
- 适用于需要可信度评估的医疗影像自动化分析场景。
超声心动图在提取左心室容积、射血分数等关键临床参数方面至关重要，这些参数用于判断心脏疾病的存在与严重程度。自动化计算这些参数时，不确定性估计对评估其可靠性极为重要。由于临床参数通常由分割图推导得出，现有方法难以将像素级不确定性有效转化为下游临床指标的不确定性。本文提出一种基于轮廓的新不确定性估计方法：直接预测轮廓位置的不确定性，并从中采样得到多组轮廓；利用这些轮廓样本可将不确定性传播至临床指标。该方法在两个心脏超声数据集上均实现了轮廓和临床指标的准确不确定性估计。代码已开源：https://github.com/ThierryJudge/contouring-uncertainty。
原文摘要 · Abstract (English)
Echocardiography plays a fundamental role in the extraction of important clinical parameters (e.g. left ventricular volume and ejection fraction) required to determine the presence and severity of heart-related conditions. When deploying automated techniques for computing these parameters, uncertainty estimation is crucial for assessing their utility. Since clinical parameters are usually derived from segmentation maps, there is no clear path for converting pixel-wise uncertainty values into uncertainty estimates in the downstream clinical metric calculation. In this work, we propose a novel uncertainty estimation method based on contouring rather than segmentation. Our method explicitly predicts contour location uncertainty from which contour samples can be drawn. Finally, the sampled contours can be used to propagate uncertainty to clinical metrics. Our proposed method not only provides accurate uncertainty estimations for the task of contouring but also for the downstream clinical metrics on two cardiac ultrasound datasets. Code is available at: https://github.com/ThierryJudge/contouring-uncertainty.
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