Uncertainty Propagation for Echocardiography Clinical Metric Estimation via Contour Sampling
This work addresses uncertainty estimation for automated clinical parameter extraction in echocardiography, which is crucial for assessing utility in heart-related conditions, but it is incremental as it builds on existing contouring and uncertainty propagation techniques.
The paper tackled the problem of estimating uncertainty in clinical metrics derived from echocardiography segmentation by proposing a contour-based method that predicts contour location uncertainty and propagates it to metrics like left ventricular volume and ejection fraction, achieving accurate uncertainty estimations on two cardiac ultrasound datasets.
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.