Uncertainty Propagation for Echocardiography Clinical Metric Estimation via Contour Sampling

2025-02-18

AI Summary

Accurate uncertainty quantification is critical for automated estimation of echocardiographic clinical parameters (e.g., left ventricular volume, ejection fraction), yet existing methods struggle to propagate pixel-level segmentation uncertainty effectively to downstream quantitative metrics. To address this, we propose a novel contour-based uncertainty propagation paradigm: first explicitly modeling uncertainty in contour localization, then generating multiple geometrically consistent contours via Monte Carlo sampling to recompute clinical metrics and aggregate their uncertainties. This approach bypasses reliance on segmentation masks, enabling end-to-end uncertainty propagation directly from image to clinical index. Evaluated on two public cardiac ultrasound datasets, our method significantly improves both contour localization uncertainty calibration and clinical metric uncertainty calibration. It delivers interpretable, high-fidelity confidence estimates—providing essential reliability guarantees for clinical deployment of AI-assisted echocardiographic diagnosis.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Computer Vision: Segmentation

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• Uncertainty estimation in echocardiography metrics
• Transforming pixel uncertainty to clinical metrics
• Novel contour-based method for uncertainty propagation

Innovation

• Contour-based uncertainty estimation
• Sampling for clinical metrics
• Propagation to downstream analysis