Abstract
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: this https URL.
Abstract (translated)
超声心动图在提取重要临床参数（例如左心室容积和射血分数）方面起着核心作用，这些参数用于确定心脏相关疾病的性质和严重程度。当部署自动技术来计算这些参数时，不确定性估计对于评估其效用至关重要。由于临床参数通常由分割地图导出，因此没有明确的方法将像素级别的不确定性值转换为下游临床度量计算中的不确定性估算。 在本工作中，我们提出了一种基于轮廓而不是分割的新型不确定性估计算法。我们的方法能够直接预测轮廓位置的不确定性，并从中抽取轮廓样本。最终，可以利用这些抽样轮廓来传播到下游临床指标中的不确定性。所提出的这种方法不仅为轮廓任务提供了准确的不确定性估计，而且在两个心脏超声数据集上的下游临床度量上也表现出色。 代码可在此网址获取：[此链接](this https URL)。
URL
https://arxiv.org/abs/2502.12713
