# Uncertainty Estimation in Deep 2D Echocardiography Segmentation

arXiv: https://arxiv.org/abs/2005.09349 · HTML: https://mlsanity.com/paper/2005.09349/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 5 of 5)

- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)
- [Scribble-based fast weak-supervision and interactive corrections for segmenting whole slide images](https://mlsanity.com/paper/2402.08333/) (2024)
- [Uncertainty Propagation for Echocardiography Clinical Metric Estimation via Contour Sampling](https://mlsanity.com/paper/2502.12713/) (2025)
- [Uncertainty in Extreme Multi-label Classification](https://mlsanity.com/paper/2210.10160/) (2022)
- [Automated Quality Assessment of Blind Sweep Obstetric Ultrasound for Improved Diagnosis](https://mlsanity.com/paper/2603.25886/) (2026)

## Abstract (arXiv)

2D echocardiography is the most common imaging modality for cardiovascular diseases. The portability and relatively low-cost nature of Ultrasound (US) enable the US devices needed for performing echocardiography to be made widely available. However, acquiring and interpreting cardiac US images is operator dependent, limiting its use to only places where experts are present. Recently, Deep Learning (DL) has been used in 2D echocardiography for automated view classification, and structure and function assessment. Although these recent works show promise in developing computer-guided acquisition and automated interpretation of echocardiograms, most of these methods do not model and estimate uncertainty which can be important when testing on data coming from a distribution further away from that of the training data. Uncertainty estimates can be beneficial both during the image acquisition phase (by providing real-time feedback to the operator on acquired image's quality), and during automated measurement and interpretation. The performance of uncertainty models and quantification metric may depend on the prediction task and the models being compared. Hence, to gain insight of uncertainty modelling for left ventricular segmentation from US images, we compare three ensembling based uncertainty models quantified using four different metrics (one newly proposed) on state-of-the-art baseline networks using two publicly available echocardiogram datasets. We further demonstrate how uncertainty estimation can be used to automatically reject poor quality images and improve state-of-the-art segmentation results.