# Uncertainty Propagation for Echocardiography Clinical Metric Estimation via Contour Sampling

arXiv: https://arxiv.org/abs/2502.12713 · HTML: https://mlsanity.com/paper/2502.12713/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 39 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [A Probabilistic U-Net for Segmentation of Ambiguous Images](https://mlsanity.com/paper/1806.05034/) (2018)
- [Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks](https://mlsanity.com/paper/1807.07356/) (2018)
- [Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography](https://mlsanity.com/paper/1908.06948/) (2019)
- [Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference](https://mlsanity.com/paper/1506.02158/) (2015)
- [PHiSeg: Capturing Uncertainty in Medical Image Segmentation](https://mlsanity.com/paper/1906.04045/) (2019)
- [Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty](https://mlsanity.com/paper/2006.06015/) (2020)
- [Numerical Coordinate Regression with Convolutional Neural Networks](https://mlsanity.com/paper/1801.07372/) (2018)
- [Multivariate Uncertainty in Deep Learning](https://mlsanity.com/paper/1910.14215/) (2019)
- [Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis](https://mlsanity.com/paper/2203.10977/) (2022)
- [Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study](https://mlsanity.com/paper/2012.15772/) (2020)
- [CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation](https://mlsanity.com/paper/2206.07664/) (2022)
- [Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization](https://mlsanity.com/paper/2109.09533/) (2021)
- [Uncertainty Estimation for Heatmap-based Landmark Localization](https://mlsanity.com/paper/2203.02351/) (2022)
- [Uncertainty Estimation in Deep 2D Echocardiography Segmentation](https://mlsanity.com/paper/2005.09349/) (2020)
- [Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography](https://mlsanity.com/paper/2207.06330/) (2022)

## Built on by (most cited first, 1 of 1)

- [The segmentation ceiling: why explicit left-ventricular masks do not improve learned ejection-fraction regression](https://mlsanity.com/paper/2609.19730/) (2026)

## Abstract (arXiv)

Echocardiography plays a fundamental role in the extraction of 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 highly valuable 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 one public and two private cardiac ultrasound datasets containing a total of 1264 patients. Code is available at: https://github.com/ThierryJudge/contouring-uncertainty.