Uncertainty Propagation for Echocardiography Clinical Metric Estimation via Contour Sampling

TL;DR

This paper introduces a novel contour-based uncertainty estimation method for echocardiography that improves the accuracy of clinical metric uncertainty propagation, aiding in better assessment of cardiac health.

Contribution

It proposes a new contouring approach to estimate uncertainty, enabling more accurate propagation of uncertainty to clinical metrics from segmentation maps.

Findings

Accurately estimates uncertainty in contouring tasks.

Effectively propagates uncertainty to clinical metrics.

Validated on two cardiac ultrasound datasets.

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: https://github.com/ThierryJudge/contouring-uncertainty.